AB-SEL model for the prediction of coronary heart disease

Abstract

Coronary heart disease (CHD) stands as the leading cause of silent and noncommunicable deaths. Early detection is essential for slowing the progression of death and saving lives. Machine learning is of interest to medical researchers and has been used to predict heart disease. This article proposes an Accuracy-Based Stacked Ensemble Learning (AB-SEL) Model to predict coronary heart disease while minimizing computational time. The dataset undergoes the Logistic Regression Recursive Feature Elimination (LR-RFE) method to identify the important features. LR, RF and AdaBoost classifiers are chosen to build Ensemble machine-learning models including techniques like bagging, majority voting, and stacking. Random search and grid search techniques have also been employed to optimize the hyperparameters. The authors applied the Cleveland dataset accessible on Kaggle and data scaling was performed using the standard scalar method. Performance measures were used to assess effectiveness, including accuracy, precision, recall, F1 score, and computational time, and were validated through 5-Fold cross-validation. The suggested approach achieved a 90.16% accuracy rate, requiring only 0.2 seconds of computational time, and yielded an AUC of 0.892.