Diabetes Prediction Empowered with Multi-level Data Fusion and Machine Learning

Abstract

Technology improvements have benefited the medical industry, especially in the area of diabetes prediction. In order to find patterns and risk factors related to diabetes, machine learning and Artificial Intelligence (AI) are vital in the analysis of enormous volumes of data, including medical records, lifestyle variables, and biomarkers. This makes it possible for tailored management and early discovery, which might revolutionize healthcare. This study examines how machine learning algorithms may be used to identify diseases, with an emphasis on diabetes prediction. The Proposed Diabetes Prediction Empowered with Mutli-level Data Fusion and Machine Learning (DPEMDFML) model combines two distinct types of models-the Artificial Neural Network (ANN) and the Support Vector Machine (SVM)-to create a fused machine learning technique. Two separate datasets were utilized for training and testing the model in order to assess its performance. To ensure a thorough evaluation of the model's prediction ability, the datasets were split in two experiments in proportions of 70:30 and 75:25, respectively. The study's findings were encouraging, with the ANN algorithm obtaining a remarkable accuracy of 97.43%. This indicates that the model accurately identified instances of diabetes, indicating a high degree of accuracy. A more thorough knowledge of the model's prediction ability would result from further assessment and validation of its performance using various measures.

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Diabetes Prediction Empowered with Multi-level
Data Fusion and Machine Learning
Ghofran Bassam1, Amina Rouai2, Reyaz Ahmad3, Muhammd Adnan Khan4
School of Computing, Skyline University College, Sharjah, United Arab Emirates1, 2, 4
Department of General Education, Skyline University College, Sharjah, United Arab Emirates3
Riphah School of Computing & Innovation-Faculty of Computing, Riphah International University, Lahore, Pakistan4
Department of Software-Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, Republic of Korea4
Abstract—Technology improvements have benefited the
medical industry, especially in the area of diabetes prediction. In
order to find patterns and risk factors related to diabetes,
machine learning and Artificial Intelligence (AI) are vital in the
analysis of enormous volumes of data, including medical records,
lifestyle variables, and biomarkers. This makes it possible for
tailored management and early discovery, which might
revolutionize healthcare. This study examines how machine
learning algorithms may be used to identify diseases, with an
emphasis on diabetes prediction. The Proposed Diabetes
Prediction Empowered with Mutli-level Data Fusion and
Machine Learning (DPEMDFML) model combines two distinct
types of models—the Artificial Neural Network (ANN) and the
Support Vector Machine (SVM)—to create a fused machine
learning technique. Two separate datasets were utilized for
training and testing the model in order to assess its performance.
To ensure a thorough evaluation of the model's prediction ability,
the datasets were split in two experiments in proportions of 70:30
and 75:25, respectively. The study's findings were encouraging,
with the ANN algorithm obtaining a remarkable accuracy of
97.43%. This indicates that the model accurately identified
instances of diabetes, indicating a high degree of accuracy. A
more thorough knowledge of the model's prediction ability would
result from further assessment and validation of its performance
using various measures.
Keywords—Disease prediction; machine learning (ML); fused
approach; artificial neural network (ANN); support vector machine
(SVM); disease diagnosis; healthcare
I. INTRODUCTION
The chronic metabolic condition known as diabetes affects
millions of people worldwide. The World Health Organization
projects that by 2030, 643 million people worldwide will have
diabetes, up from an expected 537 million in 2021 [1].
Diabetes is brought on by abnormalities in insulin synthesis or
function, which hinder the body from effectively managing
blood sugar levels. All ages are impacted, and if it is not
treated, it might have detrimental implications on one's health.
The body's immune system wrongly assaults and destroys
pancreatic insulin-producing cells in autoimmune type 1
diabetes [2]. It usually appears during childhood or
adolescence and necessitates lifelong insulin medication.
Obesity, inactivity, and poor eating habits are commonly
linked to the majority of type 2 diabetes cases [3]. Type 2
diabetes is differentiated by a decrease in the body's ability to
produce enough insulin to maintain normal blood sugar levels
or by an increase in insulin resistance [3]. Numerous
consequences can result from unmanaged diabetes. For
diabetics, cardiovascular disease, such as heart attacks and
strokes, is a major worry. Kidney issues, nerve damage
(neuropathy), retinopathy, and foot issues are some of the
consequences of diabetes [4]. One's quality of life may be
significantly impacted by these problems, which need
continual medical care. Traditional diabetes prediction
systems confront a number of problems. These techniques
frequently depend on simplistic statistical models or
rudimentary machine learning algorithms, which are incapable
of capturing the intricate interplay of many risk variables.
Furthermore, these techniques may underutilize the potential
of accessible data sources such as patient medical records,
genetic information, lifestyle variables, and environmental
factors. As a result, the accuracy and reliability of diabetes
prediction using these traditional methods are inadequate. A
subset of artificial intelligence called machine learning has
completely changed several industries, including the
healthcare industry. It involves developing algorithms and
models that are able to absorb knowledge from data and act or
anticipate without being explicitly programmed. The medical
sector's decision-making processes for disease prediction,
diagnosis, and treatment have showed great promise when
using machine learning techniques. Researchers have
investigated the merging of different ML methods for diabetes
prediction in order to overcome the limitations of existing
methodologies (Table I). Fusing several algorithms enables for
the use of each method's distinct strengths while correcting for
their particular flaws and improving forecast accuracy. A
fused machine learning model can give a more thorough and
holistic view of the condition by merging diverse data sources
such as electronic health records, medical imaging, genetic
profiles, and lifestyle data [5]. An ML-based diagnostic
system can help detect diabetic patients early on which leads
improve patient outcomes and help lessen the burden of
diabetes on individuals and healthcare systems. This paper
presents a unique framework utilizing machine learning fusion
to achieve early diagnosis of diabetes patients. The system
goals to increase the accuracy and efficacy of diabetes
diagnosis by combining various machine learning algorithms
and diverse datasets. This approach leads to proactive
healthcare interventions and ultimately improves patient
outcomes.
The Proposed Diabetes Prediction Empowered with Mutli-
level Data Fusion and Machine Learning (DPEMDFML)
model framework is presenting diabetes disease prediction. It

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is carried out using the ANN and SVM algorithms, while
using two different datasets.
The IoMT is necessary for enhancing the accuracy,
reliability, and efficacy of electronic equipment in the medical
field. By integrating the existing health care assets and
medical facilities, experts are advancing a digital medical
system [6]. The control of infectious disease waves is eased by
prompt diagnosis and improved ongoing treatment. The
internet of medical things (IoMT) is a growing area of
technology that is now being used to assist Point-of-care
testing (POCT). Using the IoMT, POCT devices may operate
wirelessly and be connected to health professionals and
medical facilities [7].
Recently has been discovered that developed ANNs may
perform well in a variety of circumstances due to ANNs'
universal prediction capabilities and adaptable network
architectures [8]. The building block of the ANN created to
mimic the function of a human neuron. Also, one of the
greatest methods for analyzing data is the use of SVM. To
control data, they utilize generalization controlling [9]. SVM
is an artificial intelligence method that assigns labels to things
by learning from examples [10]. The innovative and
promising IoMT framework presented in this study represents
a significant leap forward in the realm of diabetes disease
prediction. Drawing upon the capabilities of two cutting-edge
machine learning algorithms, ANN and SVM, this framework
exemplifies the fusion of advanced technology and healthcare,
offering a transformative approach to diabetes management
and patient care. At its core, the IoMT framework capitalizes
on the vast amount of data generated by interconnected
medical devices, wearable sensors, and health monitoring
systems. By harnessing this continuous and diverse stream of
patient-specific information, healthcare providers gain
unprecedented insights into the multifaceted aspects of
diabetes, allowing for more precise, proactive, and
personalized interventions. The first pillar of the framework,
Artificial Neural Networks (ANN), represents a sophisticated
computational model inspired by the complex
interconnections of neurons in the human brain. ANN's ability
to learn from data and recognize intricate patterns and non-
linear relationships makes it an ideal candidate for diabetes
prediction. The network's architecture is meticulously
designed, leveraging multiple layers of interconnected neurons
to extract high-level features from raw input data. The ANN's
adaptability enables it to adjust its internal parameters during
the learning process, optimizing the model's performance to
achieve highly accurate diabetes predictions. In tandem with
ANN, the IoMT framework also incorporates the renowned
Support Vector Machine (SVM) algorithm, renowned for its
prowess in binary classification tasks and its ability to handle
complex decision boundaries. SVM's kernel-based approach
allows it to efficiently discover non-linear patterns in the
feature space, making it invaluable for diabetes prediction
when the relationship between features and disease occurrence
is intricate and not easily separable.
By integrating the capabilities of both ANN and SVM, the
IoMT framework achieves a powerful ensemble of predictive
models that complement each other's strengths. The diversity
of these algorithms enhances the framework's ability to
capture subtle nuances and intricate interactions within the
data, ultimately leading to more reliable and accurate diabetes
predictions. Data privacy and security are of paramount
concern within the IoMT framework. Stringent measures are
implemented to anonymize and safeguard patient information,
and access controls are enforced to protect sensitive data from
unauthorized disclosure. The framework's design ensures that
data is utilized solely for model training purposes, mitigating
the risk of data breaches and preserving patient
confidentiality. The synergistic integration of ANNs and SVM
algorithms within the IoMT framework marks a significant
step towards personalized and data-driven diabetes prediction.
With the potential to revolutionize healthcare practices, this
cutting-edge approach empowers clinicians with actionable
insights, fosters early detection, and facilitates effective
diabetes management, ultimately enhancing the quality of life
for patients worldwide.
The structure of the research paper is as follows: Section II
represents the related work. In Section III, the contribution is
presented. The detail of the proposed model is described in
Section IV. Discussion and analysis of results are discussed in
Section V. The conclusion of this research is presented in
Section VI.
II. RELATED WORK
The presented findings encompass various studies that
examined different healthcare databases and utilized diverse
approaches and strategies to make predictions. Researchers
have developed and employed a range of prediction models,
incorporating various data mining techniques, algorithmic
methods for machine learning, or even a combination of these
strategies. These studies highlight the wide array of
approaches utilized in healthcare research to enhance
prediction accuracy and improve decision-making processes.
Akkarapol and Jongsawas [11] presented a paper that
analysed a dataset comprising 50,788 records with 43
parameters. The research identified significant risk variables,
including age, BMI, overall revenue, sex, heart attack history,
marital status, dentist check-up frequency, and diagnosis of
asthma. Other risk factors such as hypertension and
cholesterol were also recognized. The study's overall
reliability was reported as 77.11%, indicating a moderate level
of consistency in the findings. Furthermore, the true negative
rate specifically for the Artificial Neural Network (ANN)
model was noted as 79.45%, indicating its ability to accurately
identify negative cases.
Kavakiotis et al.'s paper [12] focused on evaluating data
mining and machine learning techniques for DM research.
Through the systematic comparison of three algorithms,
including Logistic Regression, Naive Bayes, and SVM, using
10-fold cross-validation, the study concluded that SVM
achieved the highest accuracy rate of 84%. These findings
contribute to the understanding of algorithm selection in DM
research, highlighting the potential benefits of SVM in
achieving accurate predictions and improving decision-
making processes.
Xue-Hui Meng et al.'s study [13] focused on comparing
the performance of decision tree models, ANNs, and logistic

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regression in diagnosing diabetes or prediabetes based on
general risk variables. The logistic regression model achieved
a classification accuracy of 76.13%, indicating its ability to
correctly classify individuals as having diabetes or prediabetes
based on the general risk variables considered in the study.
The decision tree model (C5.0) demonstrated a slightly higher
classification accuracy of 77.87%. It also showed a relatively
high sensitivity of 80.68%, meaning it successfully identified
a large proportion of True Positive (TP) cases, and a
specificity of 75.13%, indicating its capability to accurately
identify True Negative (TN) cases. In contrast, the ANN
model obtained a lower classification accuracy of 73.23%,
suggesting that it was less effective in predicting the disease
outcomes using the same set of general risk variables.
The research work conducted by Md. Faisal Faruque,
Asaduzzaman, and Iqbal [14] focused on exploring the
relationship between Diabetes Mellitus and multiple risk
factors through the analysis of 16 attributes including factors
such as age, diet, hypertension, vision problems, and genetic
predisposition. By utilizing four popular machine learning
algorithms, the researchers examined data from 200 patients.
The findings of the study indicated that the Decision Tree
algorithm demonstrated superior predictive performance
compared to Support Vector Machine (SVM), Naive Bayes
(NB), and K-Nearest Neighbour (KNN) algorithms in this
particular study, suggesting its potential efficacy in predicting
or classifying the disease based on the identified risk factors.
TABLE I. LIMITATIONS OF THE PREVIOUS WORKS
Research Study
Method
Accuracy
Limitation
Akkarapol and
Jongsawas [11]
77.11%
- Low
accuracy
- Limited to a
specific region
Kavakiotis et al.
[12]
84%
- Used three
algorithms but
the accuracy is
low.
Xue-Hui Meng et
al. [13]
Logistic Regression
Model
Decision Tree
Model (C5.0)
Artificial Neural
Networks (ANN)
Model
76.13%
77.87%
73.23%
- Low
accuracy
- Limited
features of the
dataset used
Md. Faisal
Faruque,
Asaduzzaman,
and Iqbal [14]
Decision Tree
Algorithm
Support Vector
Machine (SVM)
Naive Bayes (NB)
K-Nearest
Neighbour (KNN)
Not specified
- Small
sample size
- Limited
features
Dey et al. [15]
ANN Model with
MMS
82.35%
- Limited
evaluation
matrices
Pradhan et al.
[16]
Ensemble Learning
Approach
Not specified
- Multiple
algorithms
without
mentioning
accuracies
The study conducted by Dey et al. [15] utilized four well-
known supervised machine learning algorithms: SVM, KNN,
Naive Bayes, and ANN with MMS. These algorithms were
selected for their ability to learn from labelled data and make
predictions based on learned patterns and relationships to
analyse the Pima Indian dataset. The study revealed that the
ANN model with MMS achieved the highest accuracy rate of
82.35%, indicating its potential effectiveness in predicting the
specific outcome compared to the other four algorithms
examined.
Pradhan et al. research [16] employed supervised learning,
which involves training models on labelled data to make
predictions, to develop models for diabetes diagnosis.
Additionally, they utilized hybrid learning, which combines
multiple learning techniques, to further enhance the
performance of the diagnostic models. Finally, the researchers
explored ensemble learning, a powerful approach that
combines the predictions of multiple individual models, to
create a more robust and accurate diabetes diagnosis model.
The results of the study demonstrated that the ensemble
learning approach surpassed both supervised learning and
hybrid learning in terms of accuracy.
III. CONTRIBUTION
In contrast to previous research, this Diabetes Prediction
Empowered with Multi-level Data Fusion and Machine
Learning (DPEMDFML) model represents a more
comprehensive study that explores various commonly used
techniques for diabetes identification. The primary objective is
to compare the performance of these techniques and identify
the most effective one. It has been accomplished by
employing two distinct algorithms and evaluating them on two
different datasets, considering all relevant evaluation metrics.
Furthermore, this study delves into analyzing the significance
of each attribute in influencing the classification outcome.
This analysis provides valuable insights for future research to
adapt and improve the dataset, making it more informative and
suitable for diabetes diagnosis tasks.
IV. PROPOSED MODEL
The Diabetes Prediction Empowered with Multi-level Data
Fusion and Machine Learning (DPEMDFML) model
developed here seeks to predict diabetes in a smart healthcare
system utilizing data from the Internet of Medical Things
(IoMT) is divided into two stages: training and testing as
shown in Fig. 1. During the Training Phase, hospitals
(Hospitals A, B, C, and N) use IoMT devices to gather patient
data, which is subsequently recorded in their respective local
databases. This information might include vital indicators,
blood glucose levels, lifestyle information, and other
information. The 'Prediction Layer,' which houses multiple
ML models, with a focus on Support Vector Machines (SVM)
and Artificial Neural Networks (ANN), is at the core of this
phase.

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Fig. 1. Diabetes prediction empowered with multi-level data fusion and machine learning (DPEMDFML).
These models excel at classification tasks and are in
charge of learning whether a patient has diabetes depending
on the input data. Following the Prediction Layer, Fig. 1
shows the Performance Layer assesses the efficiency of the
ML models by employing measures such as accuracy, miss
rate, and sensitivity. Models that match the performance
criteria are saved in the public cloud as the “DPEMDFML
Generalized Model”, while those that fall short go through
additional training rounds to enhance accuracy.
The trained DPEMDFML Generalized Model is used in
the Testing Phase. When new patients from Hospital N seek
diabetes diagnosis, the system gathers raw data from IoMT
devices, which is then processed. Data cleansing, value
normalization, and missing data management are examples of
pre-processing operations that ensure the input data is ideal for
the ML models’ predictions.
The DPEMDFML Generalized Model is then used to
predict whether or not the patient has diabetes. This decision-

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making procedure has two results: If diabetes is predicted by
the model, the patient is directed to a specialist for prompt
medical intervention. If the model predicts a poor outcome,
the data is properly deleted, protecting patient confidentiality
and data privacy.
Because the system is distributed, various hospitals can
contribute data, resulting in a broad and complete dataset for
model training. Furthermore, the cloud-based architecture
improves accessibility and scalability, allowing the system to
meet growing data volumes as well as changing healthcare
demands. The system benefits from the capabilities of SVM
and ANN as its major ML models in pattern recognition,
feature extraction, and classification, results in accurate
diabetes predictions. Furthermore, the system's iterative
training technique allows for continuous development,
keeping the models current with medical advances.
The relevance of this ML-driven approach resides in its
potential to improve diabetes diagnosis and patient treatment.
The approach leverages the available information by utilizing
data from IoMT devices across many hospitals, resulting in
more reliable and exact predictions. The capacity to detect
diabetic patients quickly and give early medical treatment
assures improved disease control and perhaps improves
patient outcomes. As the system evolves, its influence on the
healthcare environment is expected to go beyond diabetes
diagnosis, with the ability to tackle additional medical
difficulties utilizing a similar distributed, ML-based approach.
The distributed, cloud-based machine learning system for
diabetes detection using IoMT data is a potential improvement
in healthcare technology. Its training and testing phases, which
are supported by SVM and ANN models, show that it can
handle complicated medical data and make correct
predictions. As the system evolves via iterative training and
embraces an ever-growing dataset, it is positioned to impact
the future of medical diagnosis, eventually improving patient
care and contributing to the healthcare industry's continuing
transformation.
A. Datasets
Diabetes Prediction Empowered with Mutli-level Data
Fusion and Machine Learning (DPEMDFML) Model used
two different datasets:
The primary dataset employed in this research is the PIMA
Indian Diabetes Database, accessible at the University of
California machine learning repository [14]. The dataset
encompasses information from 768 individuals, all of whom
are female, and their ages span from 21 to 81 years. For each
individual, the dataset consists of nine distinct feature
characteristics. These feature characteristics include eight
continuous quantitative variables, namely the number of
pregnancies, blood sugar level (in mg/dL), diastolic blood
pressure (in mmHg), skin fold thickness (in mm), body mass
index (BMI), serum insulin level (in mU/mL), age (in years),
and a pedigree function associated with diabetes. By utilizing
this comprehensive dataset, the study aims to explore the
relationships between these feature characteristics and
diabetes occurrence, enabling the development of predictive
models for early detection and assessment of diabetes risk in
female patients.
For the second dataset used in this paper, it is called the
"Diabetes prediction dataset," sourced from Electronic Health
Records (EHRs) [15]. The dataset encompasses information
from a substantial sample of 100,000 individuals, which were
collected from diverse healthcare providers and then
aggregated into a unified dataset. It is noteworthy that this
dataset includes both female and male participants. The
Diabetes prediction dataset consists of eight distinctive feature
characteristics for each individual. These features include age,
gender, hypertension, heart disease, smoking history, BMI
(body mass index), HBA1C level (glycated haemoglobin
level), and glucose level. By utilizing this comprehensive
dataset, the study aims to explore the relationships between
these feature characteristics and diabetes prediction. The
inclusion of both genders and the diverse range of feature
characteristics in this dataset facilitate a comprehensive
analysis, providing valuable insights into predicting diabetes
and its associated risk factors.
V. RESULTS AND DISCUSSION
This section showcases the results of diabetes prediction
using two different machine learning models: Support Vector
Machine (SVM) and Artificial Neural Network (ANN). The
prediction is conducted on two distinct datasets, and each
dataset is split into two different ratios for training and testing:
70:30 and 75:25. Then, a range of evaluation metrics are
calculated, include accuracy, miss-classification rate,
sensitivity, specificity, precision, False positive (FP) rate,
False discovery rate, False omission rate, Positive likelihood
ratio,
Negative likelihood ratio, Prevalence threshold, critical
success index, F1 Score, Mathews Correlation coefficient,
Fowlkes-Mallows Index, informedness, and Diagnostic odds
ratio. The following equations illustrate the equations used to
calculate each of these metrics, providing a clear
understanding of the underlying mathematical formulas for the
statistical measurements [17-23]. The utilization of this
diverse set of metrics ensures a comprehensive assessment of
the models’ performance, accounting for different aspects of
predictive accuracy and error rates. Python is utilized as the
simulation tool for implementing both the SVM model and
ANN model, to obtain the results.
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    
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    
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    
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 

 (12)
  
 (13)
  
󰇛󰇜󰇛󰇜󰇛󰇜󰇛󰇜 (14)
    (15)
       (16)
   
 (17)
A. DPEMDFML - SVM System Model - using Pima Diabetes
Dataset - 70:30
Using SVM model with the Pima Diabetes Dataset, the
dataset is divided as: 30% for testing (n=231) and 70% for
training (n=537) to assess the model's performance accurately.
The performance evaluation of the SVM model is depicted in
Table II and Table III, which illustrate the confusion matrix.
The confusion matrix provides crucial insights into the
model's predictive accuracy, enabling a detailed examination
of how well the SVM algorithm classifies diabetes and non-
diabetes cases in the dataset.
During the training phase, the SVM model’s predictions
for diabetes disease are presented in Table II. The training
dataset consists of 537 samples, which are further categorized
into 187 real positive samples, indicating the presence of
diabetes, and 350 real negative samples, indicating the
absence of diabetes. Among the real positive samples
(indicating the presence of diabetes), the SVM model
correctly identifies 117 samples as positive, accurately
signaling the presence of healthcare issues. However, the
model misclassifies 70 records as negatives, incorrectly
suggesting the absence of healthcare issues when there is an
actual health concern. On the other hand, among the real
negative samples (indicating the absence of diabetes), the
SVM model correctly predicts 309 samples as negative,
appropriately identifying the absence of healthcare conditions.
However, the model makes errors in 41 samples, wrongly
classifying them as positive, inaccurately indicating the
presence of a healthcare issue when there is none.
During the testing phase, the SVM model's predictions for
diabetes disease are presented in Table III. The testing dataset
consists of 231 samples, which are further categorized into 81
real positive samples, indicating the presence of diabetes, and
150 real negative samples, indicating the absence of diabetes.
Among the real positive samples (indicating the presence of
diabetes), the SVM model correctly identifies 48 samples as
positive, accurately signaling the presence of healthcare
issues. However, the model misclassifies 33 records as
negatives, incorrectly suggesting the absence of healthcare
issues when there is an actual health concern. However, the
SVM model successfully predicted 124 samples as negative,
properly recognizing the lack of medical diseases among the
genuine negative samples (showing the absence of diabetes).
But in 26 samples, the model misclassifies them as positive,
thus implying the existence of a healthcare concern when
there isn't one.
Table IV presents a comprehensive overview of the
performance of the proposed SVM model in terms of various
evaluation metrics. During the training phase, the SVM model
achieved the following percentages for each metric: 79.32%
accuracy, 20.67% miss-classification rate, 62.56% sensitivity,
88.28% specificity, 74.05% precision, 11.71% False positive
rate, 25.94% False discovery rate, 18.46% False omission rate,
534.10% Positive likelihood ratio, 478.00% Negative
likelihood ratio, 30.20% Prevalence threshold, 51.31% critical
success index, 67.82% F1 Score, 53.16% Mathews
Correlation coefficient, 68.06% Fowlkes-Mallows Index,
50.85% informedness, and 1259.68% Diagnostic odds ratio.
During the validation phase, the performance of the model is
evaluated, and the following evaluation metrics are obtained:
74.46% accuracy, 25.54% miss-classification rate, 59.25%
sensitivity, 82.66% specificity, 64.86% precision, 17.33%
False positive rate, 35.13% False discovery rate, 21.01% False
omission rate, 341.88% Positive likelihood ratio, 393.29%
Negative likelihood ratio, 35.10% Prevalence threshold,
44.86% critical success index, 61.93% F1 Score, 42.87%
Mathews Correlation coefficient, 61.99% Fowlkes-Mallows
Index, 41.92% informedness, and 693.70% Diagnostic odds
ratio.
TABLE II. SVM MODEL'S: PIMA DIABETES DATASET – TRAINING PHASE
– 70:30
Input
Total number of
samples
(537)
Result (output)
Expected output
Predicted
positive
Predicted
negative
187(positive)
117(TP)
70(FN)
350(negative)
41(FP)
309(TN)
TABLE III. SVM MODEL'S: PIMA DIABETES DATASET – TESTING PHASE –
70:30
Input
Total number of
samples
(231)
Result (output)
Expected output
Predicted
positive
Predicted
negative
81(positive)
48(TP)
33(FN)
150(negative)
26(FP)
124(TN)

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TABLE IV. SVM MODEL'S (PIMA DIABETES DATASET) EVALUATION METRICS, 70:30
Testing
Training
Accuracy
0.7445
(74.46 %)
0.7932
(79.32 %)
Miss-classification rate
0.2554
(25.54 %)
0.2067
(20.67 %)
Sensitivity
0.5925
(59.25 %)
0.6256
(62.56 %)
Specificity
0.8266
(82.66 %)
0.8828
(88.28 %)
Precision
0.6486
(64.86 %)
0.7405
(74.05 %)
False positive rate
0.1733
(17.33 %)
0.1171
(11.71 %)
False discovery rate
0.3513
(35.13 %)
0.2594
(25.94%)
false omission rate
0.2101
(21.01 %)
0.1846
(18.46 %)
Positive likelihood ration
3.4188
(341.88 %)
5.3410
(534.10 %)
Negative likelihood ratio
3.9329
(393.29 %)
4.7800
(478.00 %)
prevalence threshold
0.3510
(35.10 %)
0.3020
(30.20 %)
critical success index
0.4485
(44.859 %)
0.5131
(51.31 %)
F1 Score
0.6193
(61.93 %)
0.6782
(67.82 %)
Mathews Correlation co-efficient
0.4287
(42.87 %)
0.5316
(53.16 %)
Fowlkes-Mallows Index
0.6199
(61.99 %)
0.6806
(68.06 %)
informedness
0.4192
(41.92 %)
0.5085
(50.85 %)
Diagnostic odds ratio
6.9370
(693.70 %)
12.5968
(1259.68 %)
B. DPEMDFML - SVM System Model - using Pima Diabetes
Dataset - 75:25
Again, using SVM model with the Pima Diabetes Dataset.
The dataset is divided into 25% for testing (n=192) and 75%
for training (n=576) to assess the model's performance
accurately. The performance evaluation of the SVM model is
depicted in Table V and Table VI, which illustrate the
confusion matrix.
Table V demonstrates the performance of the SVM model
in predicting diabetic illness during the training phase. The
training dataset comprises 576 samples, with 203 being true
positive cases, indicating the presence of diabetes, and 373
being true negative cases, indicating the absence of diabetes.
For the true positive cases, the SVM algorithm successfully
identifies and correctly classifies 124 samples as positive,
meaning that it accurately detects the absence of healthcare
problems in those cases. However, the algorithm makes 79
errors by misclassifying some samples as negatives, falsely
suggesting the absence of healthcare concerns when diabetes
is actually present. Regarding the true negative cases, the
SVM model performs well by accurately predicting and
classifying 330 samples as negative, properly recognizing the
absence of diabetes and the presence of other medical issues in
those cases. Nevertheless, the model misclassifies 43 samples
as positive, falsely indicating the presence of a healthcare
issue when there is, in fact, no such health concern.

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TABLE V. SVM MODEL'S - PIMA DIABETES DATASET – TRAINING PHASE
– 75:25
Input
Total number
of samples
(576)
Result (output)
Expected output
Predicted
positive
Predicted
negative
203(positive)
124(TP)
79(FN)
373 (negative)
43(FP)
330(TN)
During the testing phase, Table VI showcases the SVM
model's predictions for diabetes disease. The testing dataset
consists of 192 samples, which are further categorized into 65
real positive samples, indicating the presence of diabetes, and
127 real negative samples, indicating the absence of diabetes.
Among the real positive samples (indicating the presence of
diabetes), the SVM model correctly identifies 36 samples as
positive, accurately signaling the absence of healthcare issues.
However, the model misclassifies 29 records as negatives,
incorrectly suggesting the presence of healthcare issues when
there is none. On the other hand, among the real negative
samples (indicating the absence of diabetes), the SVM model
correctly predicts 105 samples as negative, appropriately
identifying the presence of healthcare conditions. However,
the model makes errors in 22 samples, wrongly classifying
them as positive, inaccurately indicating the absence of a
healthcare issue when there is a health concern.
Table VII presents a comprehensive overview of the
performance of the proposed SVM model in terms of various
evaluation metrics. During the training phase, the SVM model
achieved the following percentages for each metric: 78.81%,
21.18%, 61.08%, 88.47%, 74.25%, 11.52%, 25.74%, 19.31 %,
529.86 %, 458.03 %, 30.82%, 50.40%, 67.02%, 52.17%,
67.34%, 49.55%, 1204.59%, accuracy, miss-classification
rate, sensitivity, specificity, precision, False positive rate,
False discovery rate, False omission rate, Positive likelihood
ratio, Negative likelihood ratio, Prevalence threshold, critical
success index, F1 Score, Mathews Correlation coefficient,
Fowlkes-Mallows Index, informedness, and Diagnostic odds
ratio, respectively. During the validation phase, the
performance of the model is evaluated, and the following
evaluation metrics are obtained: 73.43% accuracy, 26.56 %
miss-classification rate, 55. 38 % sensitivity, 82.67%
specificity, 62.06% precision, 17.32% False positive rate, 37.
93% False discovery rate, 21.64% False omission rate,
319.72% Positive likelihood ratio, 382.02% Negative
likelihood ratio, 35.86% Prevalence threshold, 41.37% critical
success index, 58.53% F1 Score, 39.22% Mathews
Correlation coefficient, 58.63% Fowlkes-Mallows Index,
38.06% informedness, and 592.47% Diagnostic odds ratio.
TABLE VI. SVM MODEL'S - PIMA DIABETES DATASET – TESTING PHASE – 75:25
Testing
Training
Accuracy
0.7343
(73.43 %)
0.7881
(78.81 %)
Miss-classification rate
0.2656
(26.56 %)
0.2118
(21.18 %)
Sensitivity
0.5538
(55.38 %)
0.6108
(61.08 %)
Specificity
0.8267
(82.67 %)
0.8847
(88.47 %)
Precision
0.6206
(62.06 %)
0.7425
(74.25 %)
False positive rate
0.1732
(17.32 %)
0.1152
(11.52 %)
False discovery rate
0.3793
(37. 93 %)
0.2574
(25.74%)
false omission rate
0.2164
(21.64%)
0.1931
(19.31 %)
Positive likelihood ration
3.1972
(319.72 %)
5.2986
(529.86 %)
Negative likelihood ratio
3.8202
(382.02 %)
4.5803
(458.03 %)
prevalence threshold
0.3586
(35.86 %)
0.3028
(30.82 %)
critical success index
0.4137
(41.37 %)
0.5040
(50.40 %)

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F1 Score
0.5853
(58.53 %)
0.6702
(67.02 %)
Mathews Correlation co-efficient
0.3922
(39.22 %)
0.5217
(52.17 %)
Fowlkes-Mallows Index
0.5863
(58.63 %)
0.6734
(67.34 %)
informedness
0.3806
(38.06 %)
0.4955
(49.55 %)
Diagnostic odds ratio
5.9247
(592.47 %)
12.0459
(1204.59 %)
TABLE VII. SVM MODEL'S (PIMA DIABETES DATASET) EVALUATION METRICS, 75:25
Testing
Training
Accuracy
0.7343
(73.43 %)
0.7881
(78.81 %)
Miss-classification rate
0.2656
(26.56 %)
0.2118
(21.18 %)
Sensitivity
0.5538
(55.38 %)
0.6108
(61.08 %)
Specificity
0.8267
(82.67 %)
0.8847
(88.47 %)
Precision
0.6206
(62.06 %)
0.7425
(74.25 %)
False positive rate
0.1732
(17.32 %)
0.1152
(11.52 %)
False discovery rate
0.3793
(37. 93 %)
0.2574
(25.74%)
false omission rate
0.2164
(21.64%)
0.1931
(19.31 %)
Positive likelihood ration
3.1972
(319.72 %)
5.2986
(529.86 %)
Negative likelihood ratio
3.8202
(382.02 %)
4.5803
(458.03 %)
prevalence threshold
0.3586
(35.86 %)
0.3028
(30.82 %)
critical success index
0.4137
(41.37 %)
0.5040
(50.40 %)
F1 Score
0.5853
(58.53 %)
0.6702
(67.02 %)
Mathews Correlation co-efficient
0.3922
(39.22 %)
0.5217
(52.17 %)
Fowlkes-Mallows Index
0.5863
(58.63 %)
0.6734
(67.34 %)
informedness
0.3806
(38.06 %)
0.4955
(49.55 %)
Diagnostic odds ratio
5.9247
(592.47 %)
12.0459
(1204.59 %)

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C. DPEMDFML - SVM System Model - using EHRs Dataset -
70:30
The SVM model was utilized in this study with the EHRs
Dataset (Electronic Health Records Dataset). To ensure a
robust evaluation of the model's performance, the dataset was
divided into 30% for testing (n=30,000) and 70% for training
(n=70,000). To assess the effectiveness of the SVM model, its
performance was analysed using two distinct evaluation
tables: Table VIII and Table IX, both presenting the confusion
matrix.
During the training phase, the SVM model's diabetes
predictions are presented in Table VIII. The training dataset
consists of an extensive sample of 70,000 records, which are
further categorized into 5,972 instances as positive cases,
indicating the presence of diabetes, and 64,028 instances as
negative cases, indicating the absence of diabetes. Among the
actual positive cases, the SVM model correctly identifies
3,621 samples as positive, correctly indicating the absence of
healthcare issues. However, the model misclassifies 2,351
records as negative, falsely signalling the presence of
healthcare issues where there are none. On the other hand,
among the actual negative cases, the SVM model accurately
predicts 63,602 samples as negative, correctly identifying the
presence of healthcare conditions. However, the model makes
errors in 426 samples, incorrectly classifying them as positive,
falsely indicating the absence of a healthcare issue.
During the testing phase, the SVM model's predictions for
diabetes disease are displayed in Table IX. The testing dataset
comprises 30,000 samples, which are further categorized into
2,528 true positive cases, indicating the presence of diabetes,
and 27,472 true negative cases, indicating the absence of
diabetes. Among the true positive cases, the SVM model
correctly classifies 1,515 samples as positive, accurately
indicating the absence of any healthcare issues. However, the
model misclassifies 1,013 records as negative, falsely
indicating the presence of healthcare issues when there are
none. Conversely, among the true negative cases, the SVM
model accurately predicts 27,298 samples as negative,
correctly identifying the presence of healthcare conditions.
Nevertheless, the model makes errors in 174 samples,
incorrectly classifying them as positive, falsely indicating the
absence of a healthcare issue.
Table X presents a comprehensive overview of the
performance of the proposed SVM model in terms of various
evaluation metrics. During the training phase, the SVM model
achieved the following percentages for each metric: 96.03%,
3.96%, 60.63%, 99.33%, 89.47%, 0.66%, 10.52%, 3.56%,
9113.16%, 2786.65%, 9.48%, 56.59%, 72.28%, 71.77%,
73.65%, 59.96%, 22995.19%, accuracy, miss-classification
rate, sensitivity, specificity, precision, False positive rate,
False discovery rate, False omission rate, Positive likelihood
ratio, Negative likelihood ratio, Prevalence threshold, critical
success index, F1 Score, Mathews Correlation coefficient,
Fowlkes-Mallows Index, informedness, and Diagnostic odds
ratio, respectively. During the validation phase, the
performance of the model is evaluated, and the following
evaluation metrics are obtained: 96.04%, 3.95%, 59.92%,
99.36%, 89.69%, 0.63%, 10.3%, 3.57%, 9461.86%,
2777.06%, 9.32%, 56.06%, 71.85%, 71.45%, 73.31%,
59.29%, 23463.06%, accuracy, miss-classification rate,
sensitivity, specificity, precision, False positive rate, False
discovery rate, False omission rate, Positive likelihood ratio,
Negative likelihood ratio, Prevalence threshold, critical
success index, F1 Score, Mathews Correlation coefficient,
Fowlkes-Mallows Index, informedness, and Diagnostic odds
ratio, respectively.
D. DPEMDFML - SVM System Model - using EHRs Dataset
– 75:25
Here, the SVM model with the EHRs Dataset (Electronic
Health Records Dataset) is employed. To ensure a robust
assessment of the model's performance, the dataset was split
into 25% for testing (n=25,000) and 75% for training
(n=75,000). To evaluate the SVM model's effectiveness, two
different evaluation tables was used to analyse its
performance: Table XI and Table XII, which present the
confusion matrix.
During the training phase, the SVM model's diabetes
predictions are presented in Table XI. The training dataset
consists of 75,000 samples, which are further categorized into
6,409 true positive cases, indicating the presence of diabetes,
and 68,591 true negative cases, indicating the absence of
diabetes. Among the true positive cases, the SVM model
correctly identifies 3,876 samples as positive, accurately
indicating the absence of healthcare issues. However, the
model misclassifies 2,533 records as negative, falsely
signalling the presence of healthcare issues where there are
none. On the other hand, among the true negative cases, the
SVM model accurately predicts 68,111 samples as negative,
correctly identifying the presence of healthcare conditions.
However, the model makes errors in 480 samples, incorrectly
classifying them as positive, falsely indicating the absence of a
healthcare issue.
TABLE VIII. SVM MODEL'S - EHRS DIABETES DATASET – TRAINING
PHASE – 70:30
Input
Total number
of samples
(70000)
Result (output)
Expected output
Predicted
positive
Predicted
negative
5972(positive)
3621(TP)
2351(FN)
64028(negative)
426(FP)
63602(TN)
TABLE IX. SVM MODEL'S - EHRS DIABETES DATASET – TESTING PHASE
– 70:30
Input
Total number
of samples
(30000)
Result (output)
Expected output
Predicted
positive
Predicted
negative
2528(positive)
1515(TP)
1013(FN)
27472(negative)
174(FP)
27298(TN)

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TABLE X. SVM MODEL'S (EHRS DIABETES DATASET) EVALUATION METRICS, 70:30
Testing
Training
Accuracy
0.9604
(96.04 %)
0.9603
(96.03 %)
Miss-classification rate
0.0395
(3. 95 %)
0.0396
(3.96 %)
Sensitivity
0.5992
(59.92 %)
0.6063
(60.63 %)
Specificity
0.9936
(99.36 %)
0.9933
(99.33 %)
Precision
0.8969
(89.69 %)
0.8947
(89.47 %)
False positive rate
0.0063
(0.63 %)
0.0066
(0.66 %)
False discovery rate
0.1030
(10. 3 %)
0.1052
(10.52%)
false omission rate
0.0357
(3.57 %)
0.0356
(3.56 %)
Positive likelihood ration
94.6186
(9461.86 %)
91.1316
(9113.16 %)
Negative likelihood ratio
27.7706
(2777.06 %)
27.8665
(2786.65 %)
prevalence threshold
0.0932
(9.32%)
0.0948
(9.48 %)
critical success index
0.5606
(56.06 %)
0.5659
(56.59 %)
F1 Score
0.7185
(71.85 %)
0.7228
(72.28 %)
Mathews Correlation co-efficient
0.7145
(71.45 %)
0.7177
(71.77 %)
Fowlkes-Mallows Index
0.7331
(73.31 %)
0.7365
(73.65 %)
informedness
0.5929
(59.29 %)
0.5996
(59.96 %)
Diagnostic odds ratio
234.6306
(23463.06 %)
229.9519
(22995.19 %)
During the testing stage, Table XII showcases the SVM
model's diabetes predictions. The test dataset comprises
25,000 samples, split into 2,091 true positive cases (indicating
the presence of diabetes) and 22,909 true negative cases
(indicating the absence of diabetes). Among the true positive
cases, the SVM model accurately identifies 1,266 samples as
positive, correctly indicating the absence of healthcare issues.
However, the model misclassifies 825 records as negative,
erroneously suggesting the presence of healthcare issues.
Conversely, among the true negative cases, the SVM model
precisely predicts 22,758 samples as negative, correctly
recognizing the presence of healthcare conditions. However,
the model makes 151 errors, incorrectly classifying them as
positive, falsely indicating the absence of healthcare issues.
Table XIII presents a comprehensive overview of the
performance of the proposed SVM model in terms of various
evaluation metrics. During the training phase, the SVM model
achieved the following percentages for each metric: 95.98%,
4.01%, 60.47%, 99.30%, 88.98%, 0.69%, 11.01%, 3.58%,
8642.10%, 2769.42%, 9.71%, 56.26%, 72.01%, 71.44%,
73.35%, 59.77%, 21713.23%, accuracy, miss-classification
rate, sensitivity, specificity, precision, False positive rate,
False discovery rate, False omission rate, Positive likelihood
ratio, Negative likelihood ratio, Prevalence threshold, critical
success index, F1 Score, Mathews Correlation coefficient,
Fowlkes-Mallows Index, informedness, and Diagnostic odds
ratio, respectively. During the validation phase, the
performance of the model is evaluated, and the following
evaluation metrics are obtained: 96.09%, 3.90%, 60.54%,
99.34%, 89.34%, 0.65%, 10.65%, 3.49%, 9185.62%,
2839.70%, 9.44%, 56.46%, 72.17%, 71.70%, 73.54%,
59.88%, 23127.93%, accuracy, miss-classification rate,
sensitivity, specificity, precision, False positive rate, False
discovery rate, False omission rate, Positive likelihood ratio,
Negative likelihood ratio, Prevalence threshold, critical
success index, F1 Score, Mathews Correlation coefficient,
Fowlkes-Mallows Index, informedness, and Diagnostic odds
ratio, respectively.
TABLE XI. SVM MODEL'S - EHRS DIABETES DATASET – TRAINING
PHASE – 75:25
Input
Total number
of samples
(75000)
Result (output)
Expected output
Predicted
positive
Predicted
negative
6409(positive)
3876(TP)
2533(FN)
68591(negative)
480(FP)
68111(TN)

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TABLE XII. SVM MODEL'S - EHRS DIABETES DATASET – TESTING PHASE – 75:25
Input
Total number of samples (25000)
Result (output)
Expected output
Predicted positive
Predicted negative
2091(positive)
1266(TP)
825(FN)
22909(negative)
151(FP)
22758(TN)
TABLE XIII. SVM MODEL'S (EHRS DIABETES DATASET) EVALUATION METRICS, 75:25
Testing
Training
Accuracy
0.9609
(96.09 %)
0.9598
(95.98 %)
Miss-classification rate
0.0390
(3.90 %)
0.0401
(4.01 %)
Sensitivity
0.6054
(60.54 %)
0.6047
(60.47 %)
Specificity
0.9934
(99.34 %)
0.9930
(99.30 %)
Precision
0.8934
(89.34 %)
0.8898
(88.98 %)
False positive rate
0.0065
(0.65 %)
0.0069
(0.69 %)
False discovery rate
0.1065
(10. 65 %)
0.1101
(11.01%)
false omission rate
0.0349
(3.49%)
0.0358
(3.58 %)
Positive likelihood ration
91.8562
(9185.62 %)
86.4210
(8642.10 %)
Negative likelihood ratio
28.3970
(2839.70 %)
27.6942
(2769.42 %)
prevalence threshold
0.0944
(9.44 %)
0.0971
(9.71 %)
critical success index
0.5646
(56.46 %)
0.5626
(56.26 %)
F1 Score
0.7217
(72.17 %)
0.7201
(72.01 %)
Mathews Correlation co-efficient
0.7170
(71.70 %)
0.7144
(71.44 %)
Fowlkes-Mallows Index
0.7354
(73.54 %)
0.7335
(73.35 %)
informedness
0.5988
(59.88 %)
0.5977
(59.77 %)
Diagnostic odds ratio
231.2793
(23127.93 %)
217.1323
(21713.23 %)
E. DPEMDFML - ANN System Model - using Pima Diabetes
Dataset - 70:30
Shifting our focus to the second algorithm used in this
research, the Artificial Neural Network (ANN) model was
employed, and the Pima Diabetes Dataset was utilized for
evaluation. To ensure a robust assessment of the model's
effectiveness, the dataset was split into two sets: 20% for
testing (n=231) and 70% for training (n=537). To gauge the
performance of the ANN model, a detailed analysis was
conducted using two distinct evaluation tables: Table XIV and
Table XV. These tables present the confusion matrix,
providing valuable insights into the model's ability to deliver
accurate predictions during both the testing and training
phases.
During the training stage, Table XIV illustrates the ANN
model’s predictions for diabetes disease. The training dataset
consists of 537 samples, further divided into 188 true positive
cases, indicating the presence of diabetes, and 349 true
negative cases, indicating the absence of diabetes. Among the
true positive cases, the ANN model correctly identifies 157
samples as positive, accurately indicating the absence of
healthcare issues. However, the model misclassifies 31
records as negative, falsely indicating the presence of
healthcare issues. Conversely, among the true negative cases,
the ANN model accurately predicts 327 samples as negative,
correctly identifying the presence of healthcare conditions.
However, the model makes 22 errors, incorrectly classifying
them as positive, falsely indicating the absence of a healthcare
issue.
During the testing phase, the ANN model's predictions for
diabetes disease are shown in Table XV. The testing dataset
consists of 231 samples, further divided into 80 true positive
cases, indicating the presence of diabetes, and 151 true
negative cases, indicating the absence of diabetes. Among the

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true positive cases, the ANN model correctly identifies 47
samples as positive, accurately indicating the absence of
healthcare issues. However, the model misclassifies 33
records as negative, falsely signalling the presence of
healthcare issues where there are none. On the other hand,
among the true negative cases, the ANN model accurately
predicts 116 samples as negative, correctly identifying the
presence of healthcare conditions. However, the model makes
35 errors, incorrectly classifying them as positive, falsely
indicating the absence of a healthcare issue.
Table XVI provides a comprehensive summary of the
proposed ANN model's performance during the training
phase, showcasing various evaluation metrics. The
percentages for each metric achieved by the ANN model are
as follows: 90.13% accuracy, 9.86% miss-classification rate,
83.51% sensitivity, 93.69% specificity, 87.70% precision,
6.30% false positive rate, 12.29% false discovery rate, 6.30%
false omission rate, 1324.78% positive likelihood ratio,
17.59% negative likelihood ratio, 44.90% prevalence
threshold, 77.20% critical success index, 85.55% F1 Score,
78.12% Mathews Correlation coefficient, 83.78% Fowlkes-
Mallows Index, 77.20% informedness, and 7527.71%
diagnostic odds ratio. During the validation phase, the
performance of the model is evaluated, and the following
evaluation metrics are obtained: 70.56%, 29.43%, 58.75%,
76.82%, 57.31%, 23.17%, 42.68%, 23.17%, 253.46%,
53.69%, 40.96%, 35.57%, 58.02%, 35.36%, 61.55%, 35.57%,
472.03%, accuracy, miss-classification rate, sensitivity,
specificity, precision, False positive rate, False discovery rate,
False omission rate, Positive likelihood ratio, Negative
likelihood ratio, Prevalence threshold, critical success index,
F1 Score, Mathews Correlation coefficient, Fowlkes-Mallows
Index, informedness, and Diagnostic odds ratio, respectively.
TABLE XIV. ANN MODEL'S - PIMA DIABETES DATASET – TRAINING PHASE
– 70:30
Input
Total number of
samples (537)
Result (output)
Expected output
Predicted positive
Predicted
negative
188(positive)
157(TP)
31(FN)
349 (negative)
22(FP)
327 (TN)
TABLE XV. ANN MODEL'S - PIMA DIABETES DATASET – TESTING PHASE –
70:30
Input
Total number of
samples (231)
Result (output)
Expected output
Predicted positive
Predicted
negative
80(positive)
47(TP)
33(FN)
151(negative)
35(FP)
116(TN)
TABLE XVI. ANN MODEL'S (PIMA DIABETES DATASET) EVALUATION METRICS, 70:30
Testing
Training
Accuracy
0.7056
(70.56 %)
0.9013
(90.13%)
Miss-classification rate
0.2943
(29.43 %)
0.0986
(9.86 %)
Sensitivity
0.5875
(58.75 %)
0.8351
(83.51%)
Specificity
0.7682
(76.82 %)
0.9369
(93.69 %)
Precision
0.5731
(57.31 %)
0.8770
(87.70%)
False positive rate
0.2317
(23.17 %)
0.0630
(6.30 %)
False discovery rate
0.4268
(42. 68 %)
0.1229
(12.29 %)
false omission rate
0.2317
(23.17%)
0.06303
(6.30 %)
Positive likelihood ration
2.5346
(253.46 %)
13.2478
(1324.78 %)
Negative likelihood ratio
0.5369
(53.69 %)
0.1759
(17.59 %)
prevalence threshold
0.4096
(40.96 %)
0.4490
(44.90 %)
critical success index
0.3557
(35.57 %)
0.7720
(77.20 %)
F1 Score
0.5802
(58.02 %)
0.8555
(85.55 %)
Mathews Correlation co-efficient
0.3536
(35.36 %)
0.7812
(78.12 %)
Fowlkes-Mallows Index
0.6155
(61.55 %)
0.8378
(83.78 %)
Informedness
0.3557
(35.57 %)
0.7720
(77.20 %)
Diagnostic odds ratio
4.7203
(472.03 %)
75.2771
(7527.71 %)

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F. DPEMDFML - ANN System Model - using Pima Diabetes
Dataset - 75:25
Once more, the ANN model was utilized with the Pima
Diabetes Dataset. The dataset here was split into 25% for
testing (n=192) and 75% for training (n=576) to ensure a
thorough evaluation of the model's performance. The
performance metrics of the ANN model are presented in
Table XVII and Table XVIII, displaying the confusion matrix
results.
During the training phase, Table XVII showcases the ANN
model's predictions for diabetes disease. Out of the 576
samples used for training, 199 are identified as real positive
cases, and 377 as real negative cases. Among these, 172 are
correctly identified as positive, meaning no healthcare issues
have been observed, while 27 are incorrectly projected as
negatives, indicating a healthcare issue is present. Regarding
the 377 samples with negative results, indicating the presence
of a healthcare condition, 352 samples are correctly forecasted
as negative, and 25 samples are wrongly forecasted as
positive, indicating the absence of a healthcare issue.
During the testing phase, Table XVIII displays the ANN
model's predictions for diabetes disease. The dataset consists
of 192 samples, divided into 69 real positive cases and 123
real negative cases. Among these, the model correctly
identifies 45 samples as positive, indicating no healthcare
issues observed, while 24 samples are incorrectly projected as
negatives, suggesting a healthcare issue. For the 123 samples
with negative results, indicating the presence of a healthcare
condition, the model appropriately forecasts 92 as negative,
and 31 samples are wrongly forecasted as positive, indicating
the absence of a healthcare issue.
Table XIX provides a comprehensive summary of the
proposed ANN model's performance during the training
phase, showcasing various evaluation metrics. The
percentages for each metric achieved by the ANN model are
as follows: 90.97%, 9.02%, 86.43%, 93.36%, 87.30%, 6.63%,
12.96%, 5.88%, 1303.39%, 14.53%, 46.53%, 79.80%,
86.86%, 79.99%, 84.98%, 79.80%, 8969.48%, accuracy, miss-
classification rate, sensitivity, specificity, precision, False
positive rate, False discovery rate, False omission rate,
Positive likelihood ratio, Negative likelihood ratio, Prevalence
threshold, critical success index, F1 Score, Mathews
Correlation coefficient, Fowlkes-Mallows Index,
informedness, and Diagnostic odds ratio, respectively. During
the validation phase, the performance of the model is
evaluated, and the following evaluation metrics are obtained:
71.35%, 28.64%, 65.21%, 74.79%, 59.21%, 25.20%, 40.78%,
20.68%, 258.76%, 46.50%, 45.21%, 40.01%, 62.06%,
39.26%, 61.10%, 40.01%, 556.45%, accuracy, miss-
classification rate, sensitivity, specificity, precision, False
positive rate, False discovery rate, False omission rate,
Positive likelihood ratio, Negative likelihood ratio, Prevalence
threshold, critical success index, F1 Score, Mathews
Correlation coefficient, Fowlkes-Mallows Index,
informedness, and Diagnostic odds ratio, respectively.
G. DPEMDFML - ANN System Model - using EHRs Dataset -
70:30
Utilizing the same algorithm, the ANN model applied to
the second dataset, referred to as the EHRs Dataset (Electronic
Health Records Dataset). To achieve a comprehensive
evaluation of the model's performance, the data set was split
as: 30% for testing (n = 30,000) and 70% for training (n =
70,000). The effectiveness of the ANN model was assessed
through a thorough analysis of its performance using two
separate evaluation tables: Table XX and Table XXI. These
tables present detailed information from the confusion matrix,
offering insights into the model's performance during both the
testing and training phases.
During the training phase, Table XX displays the
outcomes of the ANN model's predictions for diabetes disease.
In this phase, the model uses a dataset consisting of 70,000
samples, which are further divided into 5,972 real positive
cases and 64,028 real negative cases. Among the real positive
cases, 4,265 samples are correctly identified as positive,
indicating the absence of healthcare issues. However, 1,707
samples are incorrectly classified as negatives, implying
potential healthcare concerns. Regarding the real negative
cases, which represent the presence of a healthcare condition,
the model accurately predicts 63,938 samples as negative,
indicating the presence of healthcare issues. However, 90
samples are falsely predicted as positive, suggesting the
absence of healthcare issues, when in fact, they should have
been classified as negative.
TABLE XVII. ANN MODEL'S - PIMA DIABETES DATASET – TRAINING PHASE
– 75:25
Input
Total number
of samples
(576)
Result (output)
Expected output
Predicted
positive
Predicted
negative
199(positive)
172(TP)
27(FN)
377 (negative)
25(FP)
352(TN)
TABLE XVIII. ANN MODEL'S - PIMA DIABETES DATASET – TESTING PHASE –
75:25
Input
Total number
of samples
(192)
Result (output)
Expected output
Predicted
positive
Predicted
negative
69(positive)
45(TP)
24(FN)
123(negative)
31(FP)
92(TN)

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TABLE XIX. ANN MODEL'S (PIMA DIABETES DATASET) EVALUATION METRICS, 75:25
Testing
Training
Accuracy
0.7135
(71.35 %)
0.9097
(90.97%)
Miss-classification rate
0.2864
(28.64 %)
0.0902
(9.02 %)
Sensitivity
0.6521
(65.21 %)
0.8643
(86.43%)
Specificity
0.7479
(74.79 %)
0.9336
(93.36 %)
Precision
0.5921
(59.21 %)
0.8730
(87.30%)
False positive rate
0.2520
(25.20 %)
0.0663
(6.63 %)
False discovery rate
0.4078
(40.78 %)
0.1269
(12.96 %)
false omission rate
0.2068
(20.68 %)
0.0588
(5.88 %)
Positive likelihood ration
2.5876
(258.76 %)
13.0339
(1303.39 %)
Negative likelihood ratio
0.4650
(46.50 %)
0.1453
(14.53 %)
prevalence threshold
0.4521
(45.21 %)
0.4653
(46.53 %)
critical success index
0.4001
(40.01 %)
0.7980
(79.80 %)
F1 Score
0.6206
(62.06 %)
0.8686
(86.86 %)
Mathews Correlation co-efficient
0.3926
(39.26 %)
0.7999
(79.99 %)
Fowlkes-Mallows Index
0.6110
(61.10 %)
0.8498
(84.98 %)
informedness
0.4001
(40.01 %)
0.7980
(79.80 %)
Diagnostic odds ratio
5.5645
(556.45 %)
89.6948
(8969.48 %)
During the testing phase, Table XXI demonstrates the
ANN model's performance in predicting diabetes disease. The
dataset used for testing consists of 30,000 samples, which are
further divided into 2,528 actual positive cases and 27,472
actual negative cases. The model correctly identifies 1,754
positive cases, indicating the absence of healthcare issues.
However, it mistakenly classifies 774 positive cases as
negative, suggesting possible healthcare concerns. For the
actual negative cases, which indicate the presence of
healthcare conditions, the model accurately predicts 27,368
samples as negative. This demonstrates its ability to identify
the presence of healthcare issues correctly. Nevertheless, there
are 104 false positive predictions, where the model incorrectly
identifies cases as negative, indicating the absence of
healthcare issues when they should have been classified as
positive.
Table XXII provides a comprehensive summary of the
proposed ANN model's performance during the training
phase, showcasing various evaluation metrics. The
percentages for each metric achieved by the ANN model are
as follows: 97.43% accuracy, 2.56% miss-classification rate,
71.41% sensitivity, 99.85% specificity, 97.93% precision,
0.14% false positive rate, 2.06% false discovery rate, 2.60%
false omission rate, 50807.36% positive likelihood ratio,
28.62% negative likelihood ratio, 35.77% prevalence
threshold, 71.27% critical success index, 82.59% F1 Score,
82.43% Mathews Correlation coefficient, 97.12% Fowlkes-
Mallows Index, and 177501.51% diagnostic odds ratio.
During the validation phase, the performance of the model is
evaluated, and the following evaluation metrics are obtained:
97.07% accuracy, 2.92% miss-classification rate, 69.38%
sensitivity, 99.62% specificity, 94.40% precision, 0.37% false
positive rate, 5.59% false discovery rate, 2.75 % false
omission rate, 18327.76% positive likelihood ratio, 30.733%
negative likelihood ratio, 34.88% prevalence threshold,
69.00% critical success index, 79.98% F1 Score, 79.52%
Mathews Correlation coefficient, 96.73% Fowlkes-Mallows
Index, and 59634.60% diagnostic odds ratio.
TABLE XX. ANN MODEL'S - EHRS DIABETES DATASET – TRAINING
PHASE – 70:30
Input
Total number
of samples
(70000)
Result (output)
Expected output
Predicted
positive
Predicted
negative
5972(positive)
4265 (TP)
1707 (FN)
64028 (negative)
90 (FP)
63938(TN)

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TABLE XXI. ANN MODEL'S - EHRS DIABETES DATASET – TESTING PHASE – 70:30
Input
Total number of samples (30000)
Result (output)
Expected output
Predicted positive
Predicted negative
2528(positive)
1754 (TP)
774 (FN)
27472(negative)
104 (FP)
27368 (TN)
TABLE XXII. ANN MODEL'S (EHRS DIABETES DATASET) EVALUATION METRICS, 70:30
Testing
Training
Accuracy
0.9707
(97.07 %)
0.9743
(97.43 %)
Miss-classification rate
0.0292
(2.92 %)
0.0256
(2.56 %)
Sensitivity
0.6938
(69.38 %)
0.7141
(71.41 %)
Specificity
0.9962
(99.62 %)
0.9985
(99.85 %)
Precision
0.9440
(94.40 %)
0.9793
(97.93 %)
False positive rate
0.0037
(0.37 %)
0.0014
(0.14 %)
False discovery rate
0.0559
(5.59 %)
0.0206
(2.06 %)
false omission rate
0.0275
(2.75 %)
0.0260
(2.60 %)
Positive likelihood ration
183.2776
(18327.76 %)
508.0736
(50807.36 %)
Negative likelihood ratio
0.30733
(30.733 %)
0.2862
(28.62 %)
prevalence threshold
0.3488
(34.88 %)
0.3577
(35.77 %)
critical success index
0.6900
(69.00 %)
0.7127
(71.27 %)
F1 Score
0.7998
(79.98 %)
0.8259
(82.59 %)
Mathews Correlation co-efficient
0.7952
(79.52 %)
0.8243
(82.43 %)
Fowlkes-Mallows Index
0.9673
(96.73 %)
0.9712
97.12 %)
Informedness
0.6900
(69.00 %)
0.7127
(71.27 %)
Diagnostic odds ratio
596.3460
(59634.60 %)
1775.0151
(177501.51 %)
H. DPEMDFML - ANN System Model - using EHRs Dataset -
75:25
In this study, the Artificial Neural Network (ANN) model
was utilized to analyse the Electronic Health Records Dataset
(EHRs Dataset). To ensure a rigorous evaluation of the
model's capabilities, the dataset was split into 25% for testing,
comprising 25,000 samples, and 75% for training, with 75,000
samples. The effectiveness of the ANN model was thoroughly
assessed using two distinct evaluation tables: Table XXIII and
Table XXIV, which offer a detailed view of the confusion
matrix and facilitate an in-depth analysis of the model's
performance.
During the training phase, Table XXIII depicts the
predictions made by the ANN model for diabetes disease. The
dataset used for training consists of 75,000 samples, which are
further categorized into 6,409 real positive cases and 68,591
real negative cases. The model accurately identified 4,582
samples as truly positive, indicating the absence of healthcare
issues. However, it misclassified 1,827 records as negatives,
falsely signalling the presence of a healthcare condition. Out
of the 68,591 negative results, which indicate the presence of a
healthcare condition, the model correctly forecasted 68,472
samples as negative, demonstrating its effectiveness in
correctly identifying such cases. However, there were 119
samples that were inaccurately forecasted as positive,
indicating the absence of a healthcare issue when it was
present.
During the testing phase, Table XXIV presents the
predictions made by the ANN model for diabetes disease. The
dataset used for testing comprises 25,000 samples, which are
further divided into 2,091 real positive cases and 22,909 real
negative cases. The model accurately identified 1,461 samples
as truly positive, indicating the absence of healthcare issues.
However, it misclassified 630 records as negatives, falsely
signaling the presence of a healthcare condition. Out of the
22,909 negative results, which indicate the presence of a
healthcare condition, the model correctly forecasted 22,827
samples as negative, demonstrating its effectiveness in
correctly identifying such cases. However, there were 82

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samples that were inaccurately forecasted as positive,
indicating the absence of a healthcare issue when it was
present.
Table XXV provides a comprehensive summary of the
ANN model's performance during the training phase,
displaying various evaluation metrics. The ANN model
achieved the following percentages for each metric: 97.40%
for accuracy, 2.59% for miss-classification rate, 71.49% for
sensitivity, 99.82% for specificity, 97.96% for precision,
0.17% for the False positive rate, 2.53% for the False
discovery rate, 28.50% for the False omission rate, 41208.32%
for the Positive likelihood ratio, 28.55% for the Negative
likelihood ratio, 35.83% for the Prevalence threshold, 71.31%
for the critical success index, 82.48% for the F1 Score,
82.25% for the Mathews Correlation coefficient, 97.09% for
the Fowlkes-Mallows Index, 71.31% for informedness, and
144305.40% for the Diagnostic odds ratio. During the testing
phase, the ANN model achieved the following percentages for
each evaluation metric: 97.51% for accuracy, 2.84% for miss-
classification rate, 69.87% for sensitivity, 99.64% for
specificity, 94.68% for precision, 0.35% for the False positive
rate, 5.51% for the False discovery rate, 30.12% for the False
omission rate, 19520.38% for the Positive likelihood ratio,
30.23% for the Negative likelihood ratio, 35.11% for the
Prevalence threshold, 69.51% for the critical success index,
80.40% for the F1 Score, 79.96% for the Mathews Correlation
coefficient, 96.82% for the Fowlkes-Mallows Index, 69.51%
for informedness, and 64557.19% for the Diagnostic odds
ratio.
TABLE XXIII. ANN MODEL'S - EHRS DIABETES DATASET – TRAINING
PHASE – 75:25
Input
Total number
of samples
(75000)
Result (output)
Expected output
Predicted
positive
Predicted
negative
6409(positive)
4582 (TP)
1827 (FN)
68591 (negative)
119 (FP)
68472(TN)
TABLE XXIV. ANN MODEL'S - EHRS DIABETES DATASET – TESTING
PHASE – 75:25
Input
Total number
of samples
(25000)
Result (output)
Expected output
Predicted
positive
Predicted
negative
2091(positive)
1461 (TP)
630 (FN)
22909(negative)
82 (FP)
22827 (TN)
TABLE XXV. ANN MODEL'S (EHRS DIABETES DATASET) EVALUATION METRICS, 75:25
Testing
Training
Accuracy
0.9715
(97.51 %)
0.9740
(97.40 %)
Miss-classification rate
0.0284
(2.84 %)
0.0259
(2.59 %)
Sensitivity
0.6987
(69.87 %)
0.7149
(71.49 %)
Specificity
0.9964
(99.64 %)
0.9982
(99.82 %)
Precision
0.9468
(94.68 %)
0.9746
(97.96 %)
False positive rate
0.0035
(0.35 %)
0.0017
(0.17 %)
False discovery rate
0.0531
(5.51 %)
0.0253
(2.53 %)
false omission rate
0.3012
(30.12 %)
0.2850
(28.50 %)
Positive likelihood ration
195.2038
(19520.38 %)
412.0832
(41208.32 %)
Negative likelihood ratio
0.3023
(30.23 %)
0.2855
(28.55 %)
prevalence threshold
0.3511
(35.11 %)
0.3583
(35.83 %)
critical success index
0.6951
(69.51 %)
0.7131
(71.31 %)
F1 Score
0.8040
(80.40 %)
0.8248
(82.48 %)
Mathews Correlation co-efficient
0.7996
(79.96 %)
0.8225
(82.25 %)
Fowlkes-Mallows Index
0.9682
(96.82 %)
0.9709
97.09 %)
Informedness
0.6951
(69.51 %)
0.7131
(71.31 %)
Diagnostic odds ratio
645.5719
(64557.19 %)
1443.0540
(144305.40 %)

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The results of DPEMDFML model on the EHRs diabetes
dataset indicate that the ANN model outperformed other
algorithms in both the 70:30 and 75:25 ratio splits. With the
70:30 split, the ANN model achieved an impressive accuracy
of 97.43%, showcasing its robustness in correctly classifying
diabetes cases.
Similarly, in the 75:25 split, the ANN model maintained a
high accuracy of 97.40%, further validating its effectiveness in
handling the dataset. On the other hand, the SVM model also
showcased commendable results on the same EHRs diabetes
dataset. In the 70:30 split, the SVM model achieved an
accuracy of 96.03%, demonstrating its potential to effectively
classify diabetes cases.
In the 75:25 split, the SVM model maintained a high
accuracy of 95.98%, further highlighting its capability to
handle varying data proportions. Table XXVI show the
accuracies reached in this study.
TABLE XXVI. PERFORMANCE OF PROPOSED DPEMDFML MODEL W.R.T
PIMA DATASET AND EHRS DATASET
PIMA
dataset
70:30
EHRs
dataset
70:30
PIMA dataset
75:25
EHRs
dataset
75:25
SVM
74.46 %
96.03%
78.81%
95.98%
ANN
90.13%
97.43%
90.97%,
97.40%
Table XXVII presented provides an overall comparison of
the proposed DPEMDFML model with the previous works
mentioned. The results clearly demonstrate that the accuracy
of the proposed model has outperformed all the other
accuracies reported in the mentioned works, using both of the
employed algorithms.
TABLE XXVII. COMPARISON OF PROPOSED DPEMDFML MODEL WITH
PREVIOUS WORKS MENTIONED
Research Study
Method
Accuracy
Akkarapol and
Jongsawas [11]
77.11%
Kavakiotis et al. [12]
84%
Xue-Hui Meng et al.
[13]
 Logistic Regression
Model
 Decision Tree Model
(C5.0)
 Artificial Neural
Networks (ANN)
Model
76.13%
77.87%
73.23%
Dey et al. [15]
 ANN Model with
MMS
82.35%
Proposed DPEMDFML
model
 ANN
 SVM
97.43%
96.03%
VI. CONCLUSION
In summary, this research offers a distinctive and thorough
investigation of the application of machine learning
approaches for diabetes detection. The proposed DPEMDFML
model shows improved accuracy in predicting diabetes disease
compared to earlier efforts by using two separate algorithms
and two different datasets. The comprehensive assessment
tables show that the SVM and ANN models performed well
during both the testing and training periods. The suggested
framework's use of machine learning fusion has the potential
to diagnose diabetes earlier, resulting in proactive healthcare
treatments and better patient outcomes. This work advances
the field of diabetes diagnostic research by offering insightful
information on the efficacy of various algorithms and datasets.
The findings open the way for further study and model
enhancement, with the goal of facilitating improved and more
accurate diabetes detection in clinical situations. In future, we
will incorporate more recent datasets to enhance the study's
relevance and accuracy.
REFERENCES
[1] [Online]. Available: https://www.who.int/health-
topics/diabetes#tab=tab_1.
[2] Katsarou, A., Gudbjörnsdottir, S., Rawshani, A., Dabelea, D., Bonifacio,
E., Anderson, B. J. & Lernmark, A. Type 1 diabetes mellitus. Nature
reviews Disease primers, vol. 3,no. 1, pp. 1-17, 2017.
[3] https://www.cdc.gov/diabetes/basics/prediabetes.html
[4] F. Islam, R. Ferdousi, S. Rahman, and H. Y. Bushra, Computer Vision
and Machine Intelligence in Medical Image Analysis. London, U.K.:
Springer, 2019
[5] Rehman, A., Athar, A., Khan, M. A., Abbas, S., Fatima, A., & Saeed, A.
Modelling, simulation, and optimization of diabetes type II prediction
using deep extreme learning machine. Journal of Ambient Intelligence
and Smart Environments,vol. 12, no. 2, pp. 125-138, 2020.
[6] Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S. Internet of medical
things (IoMT): Applications, benefits and future challenges in healthcare
domain. J. Commun., vol. 12, no. 4, pp. 240-247, 2017.
[7] Muneer S, Rasool MA. A Enhancing Healthcare Outcomes with
Explainable AI (XAI) for Disease Prediction: A Comprehensive Review.
International Journal of Advanced Sciences and Computing, vol. 1, no.
1, pp. 37-42, 2022.
[8] Siddiqui, S. Y., Haider, A., Ghazal, T. M., Khan, M. A., Naseer, I.,
Abbas, S. & Ateeq, K. IoMT cloud-based intelligent prediction of breast
cancer stages empowered with deep learning. IEEE Access, vol. 9, pp.
146478-146491, 2021.
[9] Jakkula, V. Tutorial on support vector machine (svm). School of EECS,
Washington State University, vol. 37 no. 2, pp. 3-7, 2006.
[10] Boser, B. E., Guyon, I. M., & Vapnik, V. N.. A training algorithm for
optimal margin classifiers. In Proceedings of the fifth annual workshop
on Computational learning theory, pp. 144-152, 1992.
[11] Sa-ngasoongsong, A., & Chongwatpol, J. An analysis of diabetes risk
factors using data mining approach. Oklahoma state university, USA,
pp.1-55, 2012.
[12] Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., &
Chouvarda, I. Machine learning and data mining methods in diabetes
research. Computational and Structural Biotechnology Journal, vol. 15,
pp. -116, 2017.
[13] Meng, X. H., Huang, Y. X., Rao, D. P., Zhang, Q., & Liu, Q.
Comparison of three data mining models for predicting diabetes or
prediabetes by risk factors. The Kaohsiung Journal of Medical Sciences,
vol. 29, no., pp. 93-99, 2013
[14] Muneer SM, Alvi MB, Farrakh A. Cyber Security Event Detection
Using Machine Learning Technique. International Journal of
Computational and Innovative Sciences, vol. 2, no. 2, pp. 42-46, 2023.
[15] Dey, S. K., Hossain, A., & Rahman, M. M. Implementation of a web
application to predict diabetes disease: an approach using machine
learning algorithm. In 2018 21st international conference of computer
and information technology (ICCIT) IEEE, pp. 1-5, 2018.
[16] Pradhan, G., Pradhan, R., & Khandelwal, B. A study on various machine
learning algorithms used for prediction of diabetes mellitus. In Soft
Computing Techniques and Applications: Proceeding of the
International Conference on Computing and Communication (IC3 2020)
Springer Singapore, pp. 553-561, 2021.

IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 14, No. 10, 2023
596 | P a g e
www.ijacsa.thesai.org
[17] Abbas, T., Fatima, A., Shahzad, T., Alissa, K., Ghazal, T. M., Al-
Sakhnini, M. M., & Ahmed, A. Secure IoMT for disease prediction
empowered with transfer learning in healthcare 5.0, the concept and case
study. IEEE Access, vol. 11, pp. 39418 – 39430, 2023.
[18] Abbas, S., Issa, G. F., Fatima, A., Abbas, T., Ghazal, T. M., Ahmad, M.,
& Khan, M. A. Fused Weighted Federated Deep Extreme Machine
Learning Based on Intelligent Lung Cancer Disease Prediction Model
for Healthcare 5.0. International Journal of Intelligent Systems, vol.
2023, pp. 1-15, 2023.
[19] Asif, R. N., Abbas, S., Khan, M. A., Sultan, K., Mahmud, M., &
Mosavi, A. Development and validation of embedded device for
electrocardiogram arrhythmia empowered with transfer learning.
Computational Intelligence and Neuroscience, vol. 2022, pp. 1-14, 2022.
[20] Arooj, S., Zubair, M., Khan, M. F., Alissa, K., Khan, M. A., & Mosavi,
A. Breast cancer detection and classification empowered with transfer
learning. Frontiers in Public Health, vol. 10, pp. 1- 19, 2022.
[21] Khan, M. B. S., Nawaz, M. S., Ahmed, R., Khan, M. A., & Mosavi, A.
Intelligent breast cancer diagnostic system empowered by deep extreme
gradient descent optimization. Mathematical Biosciences and
Engineering, vol. 19, no. 8, pp. 7978-8002, 2022.
[22] Ahmad, M., Alfayad, M., Aftab, S., Khan, M. A., Fatima, A., Shoaib, B.,
& Elmitwal, N. S. Data and Machine Learning Fusion Architecture for
Cardiovascular Disease Prediction. Computers, Materials & Continua,
vol. 69, no. 2, pp. 2717-2730, 2021.
[23] Siddiqui, S. Y., Naseer, I., Khan, M. A., Mushtaq, M. F., Naqvi, R. A.,
Hussain, D., & Haider, A. Intelligent breast cancer prediction
empowered with fusion and deep learning. Computers, Materials and
Continua, vol. 67, no. 1, pp. 1033-1049, 2021.