A clinical decision support system for heart disease prediction with ensemble two-fold classification framework

Abstract

Cardiovascular disease (CVD) is a severe public health concern globally. Early and accurate CVD diagnosis is a difficult task but a necessary endeavour required to prevent further damage and protect patients’ lives. Machine Learning (ML)-based Clinical Decision Support Systems (CDSS) have the potential to assist healthcare providers in making accurate CVD diagnoses and treatments. Clinical data usually contains missing values (MVs); hence, the incorporated imputation techniques for ML have become a critical consideration when working with real-world medical datasets. Furthermore, removing instances with MVs will lead to essential data loss and produce incorrect results. To overcome these issues, this paper proposes an efficient and reliable CDSS with Ensemble Two-Fold Classification (ETC) framework for classifying heart diseases. The effectiveness of the proposed ETC framework using different supervised ML algorithms is evaluated with four distinct imputation methods for handling MVs over the standard benchmark dataset, viz., the University of California, Irwin (UCI). Experimental results show that our proposed ETC framework with the k-Nearest Neighbors(k-NN) imputation method achieves better classification accuracy of 0.9999 and a lesser error rate of 0.0989 compared to other imputation methods and classifiers with similar execution times.