Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review

Abstract

Several medical decision support systems for heart disease prediction have been developed by different researchers in today's digital and artificial intelligence-driven society to simplify and ensure effective diagnosis by utilising machine learning (ML) algorithms. To carry out a systematic comparative review of the performance of variant supervised learning ML models for heart disease prediction, and also propose a Dietary Approach to Stop Hypertension (DASH) lifestyle change therapy recommendation system blueprint for heart disease. In this research, the authors sourced 61 articles that used more than one supervised learning ML algorithms on heart disease prediction for comparison from Google Scholar and PubMed databases. A content-based filtering recommendation technique was used for designing the proposed system blueprint. Comparatively, the Voting Ensembles Classifier (VEC) algorithm demonstrated the highest accuracy. This is hinged on the fact that, although each model may slightly overfit or underfit the data, their errors can cancel out when used in combination to produce predictions that are more accurate and stable. Furthermore, VEC's more reliable predictions can improve healthcare management's overall efficiency. Lastly, this study showed the blueprint of the proposed dietary therapy recommendation system for heart disease. This research offers an extensive summary of the comparative performance of different supervised learning ML algorithms for heart disease prediction and also proposes a dietary lifestyle change therapy recommendation system framework. The information on comparative performance can aid researchers in choosing a suitable ML algorithm for their research, and the proposed system can act as a dietary therapy support tool for cardiologists when fully implemented.