An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Abstract

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart
disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method. Results were recorded
before and after the data preprocessing techniques were applied. Out of all the algorithms, gradient boosting achieved the highest accuracy rate of 92.20% for the proposed model. The proposed model yielded better results with gradient boosting in terms of precision, recall, and f1-score. It attained better prediction
performance than the existing works and can be used for other diseases that share common features using transfer learning.

Full text

An Improved Ensemble Learning Approach for Heart Disease Prediction Using
Boosting Algorithms
Shahid Mohammad Ganie
1
, Pijush Kanti Dutta Pramanik
2
, Majid Bashir Malik
3
, Anand Nayyar
4
and
Kyung Sup Kwak
5
,
*
1
School of Business, Woxsen University, Hyderabad, Telangana, 502345, India
2
School of Computing Science & Engineering, Galgotias University, Greater Noida, UP 203201, India
3
Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, 185234, India
4
Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 50000, Vietnam
5
Department of Information and Communication Engineering, Inha University, 22212, Korea
*Corresponding Author: Kyung Sup Kwak. Email: kskwak@inha.ac.kr
Received: 13 August 2022; Accepted: 03 November 2022
Abstract: Cardiovascular disease is among the top five fatal diseases that affect
lives worldwide. Therefore, its early prediction and detection are crucial, allowing
one to take proper and necessary measures at earlier stages. Machine learning
(ML) techniques are used to assist healthcare providers in better diagnosing heart
disease. This study employed three boosting algorithms, namely, gradient boost,
XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart dis-
ease-related clinical features and was sourced from the publicly available UCI ML
repository. Exploratory data analysis is performed to find the characteristics of
data samples about descriptive and inferential statistics. Specifically, it was carried
out to identify and replace outliers using the interquartile range and detect and
replace the missing values using the imputation method. Results were recorded
before and after the data preprocessing techniques were applied. Out of all the
algorithms, gradient boosting achieved the highest accuracy rate of 92.20% for
the proposed model. The proposed model yielded better results with gradient
boosting in terms of precision, recall, and f1-score. It attained better prediction
performance than the existing works and can be used for other diseases that share
common features using transfer learning.
Keywords: Heart disease prediction; machine learning classifiers; ensemble
approach; XGBoost; AdaBoost; gradient boost
1 Introduction
Heart disease is considered one of the hazards that affect human lives globally. As per the statistical
reports of different international healthcare organizations, 17.9 million (32% of all global deaths) died in
2019 because of cardiovascular diseases; this statistic has been estimated to increase to 23 million people
by 2030 [1]. Out of all the cardiovascular disease deaths, 85% are due to heart disease and stroke.
Research studies have estimated that heart disease accounts for 80% of lives in low economically
This work is licensed under a Creative Commons Attribution 4.0 International License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
DOI: 10.32604/csse.2023.035244
Article
ech
T
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developed countries and creates 85% of disabilities [1]. Detecting and predicting heart disease at earlier
stages are necessary to reduce premature deaths by a significant number in the future. The risk and
progression of heart-related diseases depend on factors such as age, changes in lifestyle, food habits, and
rapidly growing socio-economic causes, such as admission to healthcare centers [2,3]. Thus, some other
risk factors due to heart-related problems are high blood pressure, raised glucose levels, upraised blood
lipids, obesity, and being overweight.
Exploring computational intelligence techniques is needed for better prediction of heart-related diseases
so that they can be prevented and cautionary measures can be taken in advance. Furthermore, machine
learning (ML) techniques can be extensively explored to cater to healthcare resources and governance for
better patient health services. This will directly benefit hospital management, telemedicine systems,
practitioners, healthcare providers, and patient categories. In this study, we intend to develop a model for
better heart disease prediction using Ensemble Learning (EL) techniques. Specifically, considering the
criticalness of the application, we intend to improve the accuracy and other measures of the model for
heart disease prediction. Following are the novel contributions of this work:
Preprocessing of the data to improve the characteristic assessment of the dataset
Comparison of results before and after applying preprocessing techniques
Exercising feature engineering process to identify the contribution of attributes
Applying boosting algorithms using an ensemble learning approach to increase prediction accuracy
Compare the performance evaluation of the proposed model with similar research works
The rest of the article is organized as follows. Section 2 mentions the related work. Section 3 presents the
details of the proposed methodology and dataset. Section 4 presents and analyzes the experimental details
and results. Finally, Section 5 describes the conclusion and some future directions.
2 Related Work
Machine/ensemble learning techniques, with their potential to deliver consistent, reliable, and valid
results, are used in almost every sphere of life to solve real-life problems [4,5]. Copious work has been
done for disease prediction using ML and EL techniques [6]. Researchers have explored different
datasets, algorithms, and methodologies to conduct future research in diagnosing cardiovascular disease
[7,8]. Some of the important kind of literature is discussed as follows.
Latha et al. [9] experimented with different ensemble techniques, such as bagging boosting, stacking,
and a majority vote, using traditional classification algorithms to improve the efficacy of predicting
disease risk. They achieved the highest accuracy with a majority vote. Theerthagiri et al. [10] explored a
gradient boosting algorithm based on recursive feature elimination to predict heart disease based on some
medical parameters such as patient’s age, systolic and diastolic blood pressure, height, weight, smoke,
glucose/blood sugar, cholesterol, alcohol intake, smoke, and physical workout. Sultan Bin Habib et al.
[11] tried different ensemble techniques such as adaptive boosting (AdaBoost), gradient boosting machine
(GBM), light GBM (LGBM), extreme gradient boosting (XGBoost), and category boosting (CatBoost) to
predict coronary disease, considering several attributes such as gender, age, education, smoking habits,
blood pressure, hypertension, diabetes, cholesterol level, Quetelet index, heart rate, glucose level, and
chronic heart disease history. They achieved the highest accuracy with XGBoost. Budholiya et al. [12]
used an enhanced XGBoost classifier to predict heart disease effectively. The One-Hot encoding
technique was used to handle categorical features, and Bayesian optimization was used to enhance the
hyper-parameters to achieve better results. Pan et al. [13] conducted an extensive study by using a dataset
containing a good mixture of numerical and categorical attributes based on EL techniques to predict
disease. The authors observed that combining the support vector machine and AdaBoost with categorical
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attributes provides better results in predicting heart disease. Pouriyeh et al. [14] developed a framework for
the prediction/detection of heart disease by comparing conventional ML techniques with EL methods. The
dataset used for this work is taken from the online available UCI ML repository. The authors have used a
10-fold cross-validation technique to validate the results. The results showed that the support vector
machine, in combination with the boosting method, provides better results with the highest accuracy rate
of 89.12%. Moreover, bagging and stacking techniques, combined with different traditional classifiers,
improve the efficacy of overall results. Deshmukh [15] used an ensemble learning approach for heart
disease prediction. The results are compared between majority voting classifiers and the rest of the
classifiers. An extra tree classifier was used for the feature selection process. Bagged classifiers with the
majority outperformed other classifiers with the highest accuracy of 87.78%. The authors suggested that
this work can be extended using optimization procedures and new feature extraction methods. Mary et al.
[16] developed a model for heart disease prediction using ten machine learning algorithms. Among all the
considered classifiers, support vector machine yielded better results with accuracy rate of 83.49% on the
UCI dataset. The simple card algorithm increased the accuracy and reduced the prediction error rate for
other measurements. Different metrics are evaluated to validated the proposed framework. The authors
suggested that hybrid approach can be used to extend the existing work for better prediction. Alqahtani
et al. [17] proposed a framework for cardiovascular disease prediction using ensemble learning and deep
learning techniques. In the experiment, the random forest algorithm achieved the highest accuracy
(88.65%), precision (90.03), recall (88.03), f1-score (88.02), and ROC-AUC value (92). Furthermore,
feature importance was calculated to measure the risk of being involved in this disease in the future.
Kondababu et al. [18] built a model by comparing different machine learning techniques for heart disease
prediction. Seven machine learning classifiers were considered for comparative and performance analyses.
Out of all classifiers, hybrid random forest with linear model produced better results with accuracy rate of
88.4%. No data preprocessing technique was used to improve the output of proposed model. The authors
suggested that the future course of this work can done using large datasets and diverse mixture of
machine learning techniques.
Most of the work mentioned above did not sufficiently exploit data preprocessing before developing the
ensemble learning models. It resulted in inadequate outputs. Therefore, we felt the need to utilize exploratory
data analysis to improve the data quality required for the prediction model. Furthermore, data normalization
and standardization were missing in most of the existing literature, although these approaches play crucial
roles in achieving higher prediction performance.
3 Research Methodology
Fig. 1 depicts the methodology adopted for this experimental study. It presents the procedural steps that
must be executed for the early prediction of disease using various ensemble learning techniques. A publicly
available heart disease dataset has been imported into the web-based Jupyter notebook (open-source
platform) for the experimental process. The required library packages are installed from Sklearn using the
Python programming language. Initially, the boosting classifiers are applied without data preprocessing to
predict the disease. After exploratory data analysis, we found that preprocessing of data can play an
important role in attaining better results. In preprocessing phase, missing values are identified and
replaced using the data imputation method. The interquartile range method is used to detect and replace
outliers present in the dataset. Also, some other required libraries are executed to check the corrupted
data, if any, in the dataset. The dataset is split into a 70:30 ratio, where 70% is used to train the models
and 30% to test these models. To validate the results, k-fold cross-validation (K = 10) is applied. Finally,
the three considered boosting algorithms are applied after data preprocessing to obtain the desired results.
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3.1 Techniques Used
The use of ensemble learning techniques is explored in almost every field to solve real-life problems
[19]. These models have made significant progress in better prediction, detection, diagnosis, and
prognosis of different diseases. In this study, for heart disease prediction, we considered the following
three ensemble-learning-based boosting algorithms [6]:
Gradient boosting: The weak learners are trained sequentially, and all estimators are added gradually
by adapting the weights. The gradient boosting algorithm focuses on predicting the residual errors of
previous estimators and attempts to minimize the difference between the predicted and actual values.
AdaBoost: AdaBoost works by adjusting all the weights without prior knowledge of weak learners.
The base learners’weakness is measured by the estimator’s error rate while training the models.
Decision tree stumps are widely used with the AdaBoost algorithm to solve classification and
regression problems.
XGBoost: XGBoost works by combining different kinds of decision trees (weak learners) to calculate
the similarity scores independently. It helps to overcome the problem of overfitting during the training
phase by adapting the gradient descent and regularization process.
3.2 Dataset Selection
For the experiment, we used the popular dataset on heart disease, openly available in the machine
learning repository
1
at the University of California Irvine (UCI). The dataset is rich in clinical features
related to heart disease, covering wide demography. Thus, it has been one of the most popular choices for
researchers.
3.3 Attribute Information
The dataset contains 1329 instances and 14 attributes, where the first 13 attributes are predicate/
independent variables, and the last one is a dependent/target variable. The attributes are described in
Table 1. The table presents information about considered attributes, the description of attributes, their
measurements, and the value of the range.
Figure 1: Proposed methodology for research work
1
https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset.
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3.4 Dataset Description
Descriptive statistics play a vital role in identifying the data characteristics. It summarizes the data so that
understanding data becomes easier for human interpretation. Table 2 describes the statistical measurements
of the clinical attributes with their measures, such as count of records, minimum (min) value, maximum
(max) value, mean, and standard deviation (Std). For example, the age attribute has 54.41 as a mean
value and 9.07 as a standard deviation, and the maximum and minimum age numbers are 77 and
29 years, respectively. These statistical measurements are also calculated for the rest of the attributes.
3.5 Class Balance
Machine/ensemble learning models provide poor results if the dataset used is not balanced for the
problem statement. In some situations, if the target class is not equally distributed, then some sampling
techniques can be used to make a balanced dataset. The dataset for this experiment contains a good
mixture of classes, where class 1 is heart disease (691 instances) and class 0 is no heart disease
(637 instances), as shown in Fig. 2.
Table 1: Attributes information of the dataset
Attribute Description Measurement Value
range
Age Age of an individual Years 29 to 77
Sex Gender of an individual 1 = male, 0 = female 0 or 1
Cpericarditis Degree of chest pain Low, moderate, high,
extremely high
0to3
RestingBP Blood pressure of an individual while at rest
(inactive)
Hg level (in mm) 94 to 200
Cholesterol Level of serum cholesterol mg/dl 126 to
256
FastingBP Glucose level in an empty stomach (fasting) Greater than 120 mg/dl
(1 = true, 0 = false)
0or1
RestingECG Resting electrocardiographic results of an
individual while inactive
0 = normal, 1 = having ST 0 to 2
MaximumHR Highest heart rate recorded. Beats per minute 71 to 202
ExerciseIA Doing exercise with angina disease 1 = yes, 0 = no 0 or 1
Oldpeak ST depression, caused by doing exercise in
comparison to being inactive
Numeric value Relative
Slope Slope of the old peak value in the ST segment
while an individual is doing an exercise
0 = downsloping, 1 = flat,
2 = upsloping
0to2
Ca No. of major vessels colored by fluoroscopy Numeric 0 to 3
Thal Whether an individual has thalassemia or not 3 = normal, 6 = fixed defect,
7 = reversible defect
3to7
Outcome Class attribute 0 = no heart disease,
1 = heart disease
0or1
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3.6 Histogram of Dataset
A histogram is used to visualize and interpret the distribution of data samples. The representation of
histograms can be uniform, normal, left-skewed, and right-skewed. Fig. 3 depicts the normally distributed
histograms that groups all the attributes within the value range. The x-axis represents the nature of the
attribute, and the y-axis represents the value of that attribute.
3.7 Boxplot of Dataset
Fig. 4 shows the boxplots of each attribute present in the dataset. To represent boxplots for attributes, the
interquartile range method using the probability density function has been used to handle the outliers in the
dataset. For example, in fasting blood pressure, a single outlier was detected, whereas, in resting blood
pressure, multiple outliers were detected and were replaced with the z-score method.
Table 2: Dataset description
Attributes Count Mean Std Min Max
Age 1328 54.41 9.07 29 77
Sex 1328 0.69 0.46 0 1
Cpericarditis 1328 0.94 1.02 0 3
RestingBP 1328 131.61 17.51 94 200
Cholesterol 1328 246.06 51.62 126 564
FastingBP 1328 0.14 0.35 0 1
RestingECG 1328 0.52 0.52 0 2
MaximumHR 1328 149.23 22.97 71 202
ExerciseIA 1328 0.33 0.47 0 1
Oldpeak 1328 1.06 1.17 0 1
Slope 1328 1.38 0.61 0 2
Ca 1328 0.74 1.02 0 4
Thal 1328 2.32 0.61 0 3
Outcome 1328 0.52 0.49 0 1
637
691
0: No heart
disease
1: Heart
disease
Figure 2: Instances of the outcome variable
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3.8 Correlation Coefficient Analysis
The correlation coefficient analysis (CCA) method is used to identify and plot the relationship among the
dataset’s attributes [20]. A dataset is considered good if a strong association/relationship exists between the
set of independent and dependent attributes. Fig. 5 presents the CCA of all attributes used to predict disease,
and the range of relationships exists between +1 to −1 within the x-axis and y-axis. The cell value indicates
the degree of relationship between the intersecting attributes. For example, the relationship value between
resting blood pressure and age is 0.12.
4 Experiment, Results, and Discussion
This section presents the discussion on the experimental details and results achieved using boosting
algorithms for heart disease prediction. Subsequently, all results after implementing the proposed
framework are shown and analyzed systematically. The results are presented in two modules: before
preprocessing and after preprocessing for disease prediction. The evaluation is extensively discussed in
terms of performance evaluation metrics such as precision, recall, f1-score, receiver operation curve, and
traveling time of considered boosting algorithms.
Figure 3: Histogram of attributes
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Figure 4: Boxplot of attributes
Figure 5: Correlation coefficient matrix
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4.1 Data Preprocessing
Data preprocessing is vital in developing a robust and reliable system before applying ML methods to
the model [21]. In this work, missing values have been identified and replaced by the data imputation
method. Initially, we used the isnull() method to detect all the missing values and then executed the mean
and mode imputation technique with the SimpleImputer() method to fill in these missing values. This
process replaces all the missing values using the column’s mean, median, and mode. Outliers have been
detected and replaced using the Interquartile range method, where Z-score techniques were used to shift
the distribution of all the data samples and make the mean 0.
4.2 Hardware/Software Specification and Computational Time
An HP Z60 workstation was used to carry out this research work. The hardware specification of the
system is as follows: Intel XEON 2.4 GHz CPU (12 core), 4 GB RAM, 1 TB hard disk, and Windows
10 pro-64-bit. The algorithms ADB, XGB, and GB on this machine took 4.23, 3.57, and 4.51 Seconds,
respectively, for execution. Apart from hardware components, the software used for implementations is
graphical user interface-based Anaconda Navigator, web-based computing platform Jupyter Notebook,
and Python as a programming language.
4.3 Accuracy of Classifiers
The testing accuracy of boosting algorithms is shown in Fig. 6. The algorithms employed in this work
are XGBoost (XGB), AdaBoost (ADB), and gradient boosting (GB). Without applying preprocessing
techniques, the accuracy of classifiers like XGB, ADB, and GB are 87.50%, 81.50%, and 86%,
respectively. After applying preprocessing techniques, GB outperformed other boosting algorithms by
obtaining the highest accuracy rate of 92.20%, followed by AGB and ADB, both having 89.61%.
4.4 Other Measurements
The precision, recall, and f1-score of the three considered classifiers were calculated before and after
data preprocessing. The values were calculated (in percentage) for both the classes (0: no heart disease, 1:
heart disease), as shown in Figs. 7 and 8. XGB performed best for all the measurements without
preprocessing, whereas ADB performed the worst. With preprocessing, GB performed the best in most
measurements, whereas XGB and ADB achieved more or less the same results.
87.50%
89.61%
81.50%
89.61%
86.00%
92.20%
76%
78%
80%
82%
84%
86%
88%
90%
92%
94%
Before preprocessing After preprocessing
Accuracy
XGB
ADB
GB
Figure 6: Classification accuracy
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4.5 Feature Importance
Feature importance is a process to calculate the score of input features (independent predicate variables)
based on the contribution predicting the output feature (dependent/target variable) [22]. It plays an important
role in developing machine/ensemble learning models to improve prediction results. In this work, the feature
importance score (F-score) represents the number of times an attribute is used for splitting in the training
process. A higher F-score of a feature (e.g., cholesterol) indicates that it is an important attribute. Fig. 9
shows the contribution of all the attributes toward prediction in descending order based on their F-score.
For example, cholesterol has the highest significance in the prediction, whereas fasting blood pressure has
the lowest.
0.82
0.94
0.87
0.94
0.82
0.88
0.82
0.83
0.82
0.81
0.8
0.8
0.82
0.91
0.86
0.9
0.82
0.86
0.7
0.75
0.8
0.85
0.9
0.95
Precision Recall F1-score Precision Recall F1-score
0: Negative 1: Positive
XGB
ADB
GB
Figure 7: Other measurements before preprocessing
0.89
0.96
0.92
0.91
0.78
0.84
0.9
0.95
0.92
0.89
0.79
0.84
0.93
0.95
0.94
0.89
0.86
0.88
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Precision Recall F1-score Precision Recall F1-score
0: Negative 1: Positive
XGB
ADB
GB
Figure 8: Other measurements after preprocessing
Figure 9: Feature importance for prediction
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4.6 ROC Curve
The receiver operating characteristic (ROC) curve has been used to show the prediction capability of
considered boosting algorithms at different thresholds. It represents the false-positive rate vs. the true-
positive rate along the x-axis and y-axis, respectively. Using the ROC curve, we analyzed how well our
models distinguish between classes (0-no heart disease and 1-heart disease). A higher ROC curve means
that the model is predicting good results between 0’s and 1’s. If the model has AUC near 1, it means a
good separability measure; if AUC is near 0, it means the worst measure of disassociation. When the
value of AUC is 0.5, the model is not working to separate the classes effectively. The ROC curves for
ADB, XGB, and GB are shown in Figs. 10–12, respectively. From the figures, we conclude that XGB
performs best, followed by GB and ADB.
Figure 10: ROC curve for ADB
Figure 11: ROC curve for XGB
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4.7 Comparative Analysis
The proposed method produced good results in terms of different evaluation metrics for heart disease
prediction. The performance of our proposed framework has been compared with several relevant studies
in terms of techniques used, dataset, and accuracy, as shown in Table 3. Our proposed framework yielded
good results in terms of different evaluation metrics, particularly for accuracy in predicting heart disease.
Techniques such as data imputation for handling missing values, detection, and replacement of outliers
using the Boxplot method have been used to achieve better results than other related works.
Figure 12: ROC curve for GB
Table 3: Comparison of the proposed work with existing similar works
Research
work
Ensemble techniques adopted Dataset used Highest
accuracy
[11] XGB, ADB, GBM,
LGBM, and CatBoost
Framingham heart disease
dataset (publicly available)
87.62% with
XGB
[9] Boosting, bagging, stacking, and majority vote Cleveland heart disease
dataset (publicly available)
85.48% with
majority vote
[10] Recursive feature elimination and GB Do 89.78%
[12] XGB with Bayesian optimization Do 91.80%
[14] CatBoost, GB, XGB, and ADB Do 83.60% with
ADB
[17] DNN, KDNN, XGB, KNN, decision tree, and
random forest
Do 88.65% with
random forest
[18] Naïve Bayes, linear model, logistic regression,
decision tree, random forest, SVM, and HRFLM
Do 88.40% with
HRFLM
Our
method
XGB, ADB, and GB Do 92.20% for
BDT
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5 Conclusion
This study applied boosting algorithms to predict heart disease effectively. Different preprocessing
methods, such as imputation, Z-score, and cleaning methods, were employed to improve the dataset’s
prediction results and quality assessment. This study also executed three different boosting algorithms,
namely, XGBoost, AdaBoost, and gradient boosting, before and after applying preprocessing techniques.
The experimental results were assessed using different statistical/ML measurements. The experimental
results revealed that gradient boosting achieved the highest accuracy rate of 92.20%. The gradient
boosting algorithm also achieved better results for other metrics, such as precision, recall, and f1-score.
Finally, the feature importance process was employed to calculate the contribution of independent
features toward the final outcome.
Other ensemble learning techniques, such as bagging and stacking, can be used to improve the efficacy
of this work. This proposed method can be used for other healthcare datasets that share the commonality of
features to extend the scope of this research work. Deep learning techniques can also be explored to detect
and predict cardiovascular diseases better.
Funding Statement: This work was supported by National Research Foundation of Korea-Grant funded by
the Korean Government (MSIT)-NRF-2020R1A2B5B02002478.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
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