Performance Evaluation and Comparison of Heart Disease Prediction Using Machine Learning Methods with Elastic Net Feature Selection

Sanjib Ghosh^{1, 2,} and Muhammad Alamgir Islam¹

¹Assistant Professor, Department of Statistics, University of Chittagong, Chittagong 4331, Bangladesh

²Ph.D. Fellow at Zhejiang Gongshang University, China

Cite this paper

Sanjib Ghosh and Muhammad Alamgir Islam. Performance Evaluation and Comparison of Heart Disease Prediction Using Machine Learning Methods with Elastic Net Feature Selection. American Journal of Applied Mathematics and Statistics. 2023; 11(2):35-49. doi: 10.12691/AJAMS-11-2-1

Abstract

Abstract Heart disease is a fatal human disease that rapidly increases globally in developed and underdeveloped countries and causes death. This disease's timely and accurate diagnosis is essential for avoiding patient harm and preserving their lives. This study compared the classifier’s performance in three stages: complete attributes, class balance, and after-feature selection. For class balancing using SMOTE (Synthetic Minority Oversampling Technique) and Elastic Net feature selection algorithm has been used to select suitable features from the available dataset. In this study, justification of performance, the authors have used Logistic Regression (LR), K-nearest neighbor (KNN), Support vector machine (SVM), Random Forest (RF), Adaboost (AB), Artificial neural network (ANN), and Multilayer perceptron (MLP). It has been found that the performance increased ANN and LR after class balance and was unchanged in SVM and MLP. The classification accuracies of the top two classification algorithms, i.e., RF and Adaboost, on full features were 99% and 94%, respectively. After applying feature selection algorithms, the classification accuracy of RF slightly decreases from 99% to 92%. The accuracy of Adaboost decreases from 94% to 83%. However, the performance of classifiers increased after class balance and feature selection, such as KNN, SVM, and MLP. After class balancing and feature selection, we observed that the SVM classifier performs best.

Keywords

heart disease, SMOTE, elastic net, LR, KNN, SVM, DF, Adaboost, ANN, MLP

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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