HDDSS: An Enhanced Heart Disease Decision Support System Using RFE-ABGNB Algorithm

Abstract

Heart disease is the leading cause of mortality globally. Heart disease refers to a range of disorders that affect the heart and blood vessels. The risks of developing heart disease become minimized if heart disease is detected early. Previous studies have suggested many heart disease decision-support systems based on machine learning (ML) algorithms. However, the lower prediction accuracy is the main issue in these heart disease decision-support systems. The proposed work developed a heart disease decision-support system (HDDSS) that can predict whether or not a person has heart disease. The main goal of this research work is to use the RFE-ABGNB to improve HDDSS prediction accuracy. The Cleveland heart disease dataset is used for training and validating the proposed HDDSS. The two significant stages of HDDSS are the feature selection stage and the classification modeling stage. The recursive feature elimination (RFE) technique is used in the first stage of HDDSS to select the relevant features of the heart disease dataset. In the second stage of HDDSS, the proposed Adaptive boosted Gaussian Naïve Bayes (ABGNB) algorithm has been used to construct a classification model for training and validating a heart disease decision-support system. An output of HDDSS is analyzed using various classification output measures. According to the results obtained, our proposed method attained a predictive performance of 92.87 percent. This HDDSS model would perform well when compared to other heart disease decision-support systems found in the literature. According to our experimental analysis, the RFE-ABGNB focused heart disease decision-support system is more appropriate for a heart disease prediction.