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Medical Data Mining domain concerned with prediction knowledge as a method to extract desired outcomes from data for specific purposes. Anemia is one of the most common hema-tological diseases and in this study concentrate on the most five common types of anemia. This paper specifies the anemia type for the anemic patients through a predictive mode...
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... in the human blood. A Complete Blood Cell (CBC) count test conducted for patients in laboratory. The ane- mia disease types identified using this information: age, gender, hemoglobin, Hematocrit and other attribute values when it is lower a normal range Green (2012). Anemia types classification accord- ing to CBC test values illustrated in Fig. 1 (Sanap et al., ...
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... In this study, ANN, SVM, and statistical model methods were applied in the diagnosis of iron deficiency. Some classification algorithms such as NB, MP, J48, and SMO were used by using WEKA data mining tool [40]. As a result, it was observed that the J48 decision tree algorithm (JDTA) had the best performance. ...
Data mining methods are important for the diagnosis and prediction of diseases. Early and accurate diagnosis of patients is vital for their treatment. Various methods have been used in the literature to classify anemia. However, due to the different characteristics of patient datasets, changes in dataset sizes, different parameter numbers and features, and different numbers of patient records, algorithm performances vary according to datasets. In this study, the Harris hawks algorithm (HHA) and the multivariate adaptive regression spline (MARS) were used to classify anemia based on blood data of 1732 patients from the Kaggle database of patients with and without anemia. Six different algorithms were proposed to determine the parameters of the linear anemia approximation, namely multilinear form HHA, multilinear quadratic form HHA, multilinear exponential form HHA, first-order MARS model, second-order MARS model, and the best performing MARS model. The performance of the six proposed algorithms has been analyzed and found to be better than the previous studies in the literature.
... The highest accuracy (85.6%) was obtained using Bagged Decision Trees. Abdullah and Al-Asmari (2016) classified five anaemia types with seven different blood parameters using blood records from 41 anaemic patients (Abdullah & Al-Asmari, 2016). Using classification algorithms such as NB, Multilayer Perception, J48, and Sequential Minimal Optimization (SMO), the highest success was achieved with J48. ...
Anaemia occurs when the haemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients with artificial intelligence and machine learning methods, disease predictions can be made for newly ill individuals and decision‐support mechanisms can be created for physicians with these predictions. Thanks to these methods, which are very important in reducing the margin of error in the diagnoses made by doctors, the evaluation of data records in health institutions is also important for patients and hospitals. In this study, six hybrid models are proposed to classify non‐anaemia records, Hgb‐anaemia, folate deficiency anaemia (FDA), iron deficiency anaemia (IDA), and B12 deficiency anaemia by combining artificial intelligence and machine learning methods TreeBagger, Crow Search Algorithm (CSA), Chicken Swarm Optimization Algorithm (CSO) and JAYA methods. The proposed hybrid models are analysed with two different approaches, with/without applying the SMOTE technique to achieve high performance by better emphasizing the importance of parameters. To solve the multiclass anaemia classification problem, fuzzy logic‐based parameter optimization is applied to improve the class‐based accuracy as well as the overall accuracy in the dataset. The proposed methods are evaluated using ROC criteria to build a prediction model to determine the anaemia type of anaemic patients. As a result of the study on the dataset taken from the Kaggle database, it is observed that the six proposed hybrid methods outperformed other studies using the same dataset and similar studies in the literature.
... En [9] tiene un enfoque clasificación nutricional por antropometría compatible con riesgo de desnutrición crónica. Otras investigaciones como [17] que diseña un modelo que prediga el estado nutricional de niños menores de cinco años utilizando técnicas de minería de datos, u otros estudios [19,20,22,23,24] haciendo comparaciones con diferentes modelos de clasificación relacionados al problema de la anemia. ...
... En la comparación de las herramientas utilizadas en los artículos que pertenecen a este grupo, se encuentra que varias investigaciones utilizaron el software llamado Weka (Waikato Environment for Knowledge Analysis) en sus diferentes versiones, en [8] con el objetivo de conocer si un paciente necesita un seguimiento por un especialista de nutrición, en [17] diseña un modelo que prediga el estado nutricional de niños menores de cinco años utilizando técnicas de minería de datos, en [19] explora la cantidad de alimentos sobre los que se requería información sobre la ingesta para predecir con precisión el cumplimiento, o no, de las recomendaciones dietéticas clave, en [21] estudia los hábitos dietéticos relacionados con el estado de obesidad de los niños, en [22] demostrar el análisis de la desnutrición en función de la ingesta de alimentos, el índice de riqueza, el grupo de edad, el nivel educativo, la ocupación, etc. y en [23] explora la cantidad de alimentos sobre los que se requería información sobre la ingesta para predecir con precisión el cumplimiento, o no, de las recomendaciones dietéticas clave. ...
... En donde mayor cantidad de muestra fue en un estudio donde se utilizó regresión lineal y otros algoritmos, siendo 9004 datos, los cuales se recopilaron utilizando el analizador de hematología automático Mindray BC-5300 [23]. ...
One of the main public health problems is child malnutrition, since it negatively affects the individual throughout his life, limits the development of society and makes it difficult to eradicate poverty. The first objective of this research is to apply data mining techniques for preprocessing, cleaning, reduction and transformation to a data lake that has allowed analyzing anemia in children under 5 years of age, the second objective is to apply Machine Learning algorithms to obtain the best model to predict anemia in children under 5 years of age. The data set was extracted from the open data platform of the government of Peru that corresponds to South Lima, North Lima, East Lima, Central Lima and rural Lima, which collected a total of 138,369 instances and 36 variables of which 30 are categorical and 6 numeric, being an unbalanced data set. In order to obtain the best predictor variables, the Anova F-test and Chi Square filters were used, and it was possible to reduce them to 10 variables, cases were also carried out without considering one of the filters and both filters.To find the best prediction model, the algorithms have been tested: decision tree, logistic regression, K nearest neighbors, random forest and naive bayes. As a result, we show that the best algorithm to predict anemia in children under 5 years of age is the Naive Bayes algorithm with the highest recall of 74%, precision of 43% and accuracy of 70%.
... Thalassemia diagnosis depends on certain characteristics derived after performing a complete blood count (CBC) test. However, the reliability of the test can lead to the misdiagnosis of thalassemia as similar characteristics can also be observed in different blood disorders (Abdullah & Al-Asmari, 2016;Jatoi et al., 2018;Meena et al., 2019). Blood diseases can be of various types, such as anaemia, which is a common nutritional deficiency and blood disorder in childhood and infancy, and iron deficiency anemia (IRD) is mostly found in women and children, especially in developing countries (AlAgha et al., 2018;Jatoi et al., 2018). ...
... Blood diseases can be of various types, such as anaemia, which is a common nutritional deficiency and blood disorder in childhood and infancy, and iron deficiency anemia (IRD) is mostly found in women and children, especially in developing countries (AlAgha et al., 2018;Jatoi et al., 2018). However, the most crucial type of anaemia is thalassemia, an inherited disorder whose identification or differentiation from normal patients is challenging from the CBC test (Abdullah & Al-Asmari, 2016). Therefore, the problem identified in the healthcare sector is to design a model that can predict the risk of thalassemia in patients before their CBC test. ...
... Similarly, Saichanma et al. (2014) used the J48 decision tree algorithm to predict the abnormality of peripheral blood smear, focusing mainly on the attribute of RBC of the CBC test (Saichanma et al., 2014). The previous studies (Abdullah & Al-Asmari, 2016;Alaa & Shurrab, 2017;AlAgha et al., 2018;Jatoi et al., 2018;Meena et al., 2019) have classified the types of anaemia or thalassemia utilizing the techniques of data mining. However, the present study focused on determining thalassemia traits' existence based on the CBC test attributes (MCV, HGB, RDW, MCHC, and HCT) for predicting the risk of thalassemia. ...
Medical data mining is concerned with prediction knowledge, which is a useful method for extracting hidden patterns from given data for specific purposes. Thalassemia is one of the most common inherited blood hematological disorders, and this paper adopted data mining classification techniques to generate results with high performance and accuracy for risk prediction of thalassemia. The dataset for this purpose was collected from NIBD (National Institute of Blood Diseases), a well-known institute and hospital for blood diseases in Karachi, Pakistan. They provided 301 records of CBC test reports containing positive and negative statuses of diagnosis of thalassemia traits. There were many instances in the report, of which 6 were used for our research purpose, i.e. Gender, MCV, HGB, HCT, MCHC, and RDW. The dataset was divided into training and test data using the WEKA tool. Four algorithms of data mining classification, namely J48 Decision Tree, Naïve Bayesian Network, SMO algorithm, and Multilayer Perceptron Neural Network were adopted to train the model and classify the patient having traits of thalassemia from normal persons with the use of the WEKA tool. Results revealed that out of all four algorithms, Naïve Bayes provided results with the highest accuracy of 99%.
... The work [9] determined which individual classifier or subset of classifier in combination with each other achieves maximum accuracy in Red blood cell classification for anemia detection showing unique idea of use of subset of classifier and use of ensemble learning techniques. [10] specified anemia type for the anemic patients with dataset from the Complete Blood Count (CBC) which showed J48 Decision Tree as best performer. ...
Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream. This research aims to design a model for prediction of Anemia in children under 5 years of age using Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were applied. It is followed by verification, validation along with result analysis. Random Forest is the best performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms. Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5 years of age.
... Sanap et al. [137] proposed an anemia classification method based on CBC reports using C4.5 decision tree algorithm and SVM using WEKA tool with 514 instances and obtained accuracy of 99.42% and 88.13% respectively. Abdullah et al. [12] presented anemia types prediction method based on CBC reports using data mining techniques using WEKA tool for 41 patients. The experiment showed that the J48 decision tree performed better with 97% precision among Naive Bayes, MLP and SVM algorithms. ...
Anemia is a blood disorder which is caused due to inadequate red blood cells and hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in pregnant women and infants. According to the survey conducted by the World Health Organization (WHO) (McLean et al., Public Health Nutr 12(4):444–454, 2009), anemia affects 1.62 billion people constituting 24.8% of the population and is considered the world’s second leading cause of illness. The Peripheral Blood Smear (PBS) examination plays an important role in evaluating hematological disorders. Anemia is diagnosed using PBS. Being the most powerful analytical tool, manual analysis approach is still in use even though it is tedious, prone to errors, time-consuming and requires qualified laboratorians. It is evident that there is a need for an inexpensive, automatic and robust technique to detect RBC disorders from PBS. Automation of PBS analysis is very active field of research that motivated many research groups to develop methods using image processing. In this paper, we present a review of the methods used to analyze the characteristics of RBC from PBS images using image processing techniques. We have categorized these methods into three groups based on approaches such as RBC segmentation, RBC classification and detection of anemia, and classification of anemia. The outcome of this review has been presented as a list of observations.
Graphical abstract
... Random prediction (Rp) classification algorithms were used by [43] for the selection of anemia in pregnant women. The authors in [44][45][46] adopted classical approaches, i.e., WEKA, Naïve Bayes, multilayer perception, and J48 algorithms to predict anemia types using CBC reports. The authors in [47] proposed a computer-aided system to diagnose blood disorders like anemia by classifying red blood cells. ...
... Random prediction (Rp) classification algorithms were used by [43] for the selection of anemia in pregnant women. The authors in [44][45][46] adopted classical approaches, i.e., WEKA, Naïve Bayes, multilayer perception, and J48 algorithms to predict anemia types using CBC reports. The authors in [47] proposed a computer-aided system to diagnose blood disorders like anemia by classifying red blood cells. ...
The automatic detection of blood cell elements for identifying morphological deformities is still a challenging research domain. It has a pivotal role in cognition and detecting the severity level of disease. Using a simple microscope, manual disease detection, and morphological disorders in blood cells is mostly time-consuming and erroneous. Due to the overlapped structure of RBCs, pathologists face challenges in differentiating between normal and abnormal cell shape and size precisely. Currently, convolutional neural network-based algorithms are effective tools for addressing this issue. Existing techniques fail to provide effective anemia detection, and severity level prediction due to RBCs’ dense and overlapped structure, non-availability of standard datasets related to blood diseases, and severity level detection techniques. This work proposed a three tier deep convolutional fused network (3-TierDCFNet) to extract optimum morphological features and identify anemic images to predict the severity of anemia. The proposed model comprises two modules: Module-I classifies the input image into two classes, i.e., Healthy and Anemic, while Module-II detects the anemia severity level and categorizes it into Mild or Chronic. After each tier’s training, a validation function is employed to reduce the inappropriate feature selection. To authenticate the proposed model for healthy, anemic RBC classification and anemia severity level detection, a state-of-the-art anemic and healthy RBC dataset was developed in collaboration with Shaukat Khanum Hospital and Research Center (SKMCH&RC), Pakistan. To evaluate the proposed model, the training, validation, and test accuracies were computed along with recall, F1-Score, and specificity. The global results reveal that the proposed model achieved 91.37%, 88.85%, and 86.06% training, validation, and test accuracies with 98.95%, 98.12%, and 98.12% recall F1-Score and specificity, respectively.
... The study considered 12 different types anemia. [9] , [10] also presented machine learning based approaches for predicting anemic condition. ...
Anemia is very common blood disorder worldwide. Iron and B12 deficiency type of anemia are mostly observed with similar symptoms. A system is needed to diagnose anemia so that patient will get proper treatment on time. Fuzzy expert system, assisted by concern domain expert, provide effective means for conflict resolution of multiple criteria and better assessment of options. This paper presents fuzzy expert system for detection of nutritional deficiency anemia with all possible combinations. The system takes four lab parameters as input and gives output as anemia type divided into twelve different categories. Rule base is developed under the guidance of expert physician. Mamdani inference mechanism with Best of Maxima as defuzzification method is used. The system is implemented in Matlab and tested on 150 patient's data. Results of system are compared with diagnosis of expert.
... The result simulation indicated that the decision tree (DT) performed better than other learning techniques in forecasting kidney failure chronic disease. Furthermore, M. Abdullah and S. Al-Asmari in [11], clarified the same DM approaches to designate the type of anemia patients suffer from anemia. DT executed with an accuracy result of 93.75 percent. ...
Diabetes is a long-term disease. Inappropriate blood sugar level control in diabetic patients can lead to serious issues like kidney and heart diseases. Obesity is widely regarded as a major risk factor for type 2 diabetes. In this research, a model proposed to predict diabetic obese patients based on Expectation Maximization, PCA, and SMOTE Algorithms in the preprocessing and feature extraction phases, and using Fuzzy KNN classifier in the prediction phase. The model applied on real dataset and the accuracy of prediction results reflects the positive effect of the preprocessing techniques. The accuracy of the proposed model is 95.97% and outperforms other model applied on the same dataset.
