Exploring Machine Learning Techniques for Coronary Heart Disease Prediction

Abstract

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance.

A predictive analysis framework of heart disease using machine learning approaches

“…The second-best model was both LR and KNN gaining an accuracy of 86.81%. Khdair and Dasari [4] designed a classification technique using LR, SVM, KNN, and multi-layer perceptron (MLP), which are three different types of neural networks. The results of 10-fold cross-validation on the sample demonstrate that SVM marginally outperforms MLP neural network classifier, KNN, and Logistic Regression in terms of mean accuracy, which are 73.8%, 73.4%, 73.2%, and 72.7%, respectively.…”

Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

“…The number of publications on this phenomenon decreased from 12 in 2019 to 7 in 2020. CAD presence prediction 23 [25] Heart disease prediction 5 [26] Coronary heart disease prediction 24 [27] Heart disease prediction 6 [28] CHD detection 25 [29] CHD prediction 7 [30] CAD prediction 26 [31] CHD prediction 8 [32] predict coronary heart disease 27 [33] prediction of CHD 9 [16] CHD Prediction based on risk factors 28 [34] classification of coronary artery disease medical data sets [1] Accuracy of ML algorithms for predicting clinical events 29 [35] Prediction of CHD [17] methodology of predicting CHD 30 [36] CAD detection [37] CAD detection 31 [2] CHD Prediction [38] prediction of heart diseases 32 [39] Heart Disease Diagnosis [40] prediction of heart diseases 33 [41] CHD prediction [42] CAD diagnosis 34 [43] CHD prediction [44] Prediction of CHD 35 [45] NN-based prediction of CHD [46] Diagnosing CHD 36 [47] Prediction of CHD [48] prediction of heart disease 37 [49] Prediction of CHD [50] CHD Diagnosis…”

Performance Comparison between Meta-classifier Algorithms for Heart Disease Classification

“…This score allows for assessing both recall and precision simultaneously. The following formula can be used to compute [32]:…”