Diagnosis of Coronary Heart Disease via Classification Algorithms and a New Feature Selection Methodology

According to the World Health Organization (WHO), 31% of the world’s total deaths in 2016 (17.9 million) was due to cardiovascular diseases (CVD). With the development of information technologies, it has became possible to predict whether people have heart diseases or not by checking certain physical and biochemical values at a lower cost. In this study, we have evaluated a set of different classification algorithms, linear discriminant analysis and proposed a new hybrid feature selection methodology for the diagnosis of coronary heart diseases (CHD). One of the advantages of the proposed method is its ability to work on real-time datasets. Throughout this research effort, we have tested the performance of our method using publicly available heart disease datasets (UCI Machine Learning Repository, Z-Alizadehsani). We have conducted comparative performance evaluations in terms of accuracy, sensitivity, specificity, F-measure, AUC and running time.