Decision Support System for Reliable Prediction of Heart Disease using Machine Learning Techniques: An Exhaustive Survey and Future Directions

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Deepali Yewale, S. P. Vijayaragavan, Mousami Munot


MLA Style: Deepali Yewale, et al. "Decision Support System for Reliable Prediction of Heart Disease using Machine Learning Techniques: An Exhaustive Survey and Future Directions." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 316-331. Crossref,

APA Style: Deepali Yewale, Vijayaragavan, S. P., Mousami Munot.(2022). Decision Support System for Reliable Prediction of Heart Disease using Machine Learning Techniques: An Exhaustive Survey and Future Directions. International Journal of Engineering Trends and Technology, 70(4), 316-331.

The Centers for Disease Control and Prevention statistics say 17.9 million people died from cardiovascular diseases (CVD), representing 32% of global deaths. This will increase and may reach 50% in 2050. CVD continue to be the prominent cause of mortality globally, making early detection of heart disease critical. Previously, knowledge-centred clinical decision support systems were created, which applied medical professionals' expertise and manually transferred data into computer systems. This procedure is time-consuming and is highly reliant on the judgment of a medical professional, which may be subjective. Machine learning (ML) algorithms have been applied to solve this problem by automatically gaining information from raw data. This study aims to thoroughly review the decision support system (DSS) using the ML approach for the CVD prediction for the University of California Irvin (UCI) dataset. Firstly, the exhaustive survey is carried out to understand and study the approaches adopted by different researchers. In the preceding sections, a few important aspects of heart disease study are discussed, including Risk factors of heart disease, Types of heart disease, ML approaches in the design of prediction systems, and optimization techniques for performance improvement. The surveyed papers are evaluated using different performance matrices. After that, I discovered the literature gaps and presented them in the comparative analysis section. This survey will assist investigators who wish to use ML or data mining approach in Heart disease projection.

Cardiovascular disease, Heart disease prediction, Machine Learning, Decision support system.

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