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Machine Learning Techniques for Heart Disease Prediction: A Comparative Study and Analysis

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Abstract

Nowadays, people are getting caught in their day-to-day lives doing their work and other things and ignoring their health. Due to this hectic life and ignorance towards their health, the number of people getting sick increases every day. Moreover, most of the people are suffering from a disease like heart disease. Global deaths of almost 31% population are due to heart-related disease as data contributed by the World Health Organization (WHO). So, the prediction of happening heart disease or not becomes important for the medical field. However, data received by the medical sector or hospitals is so huge that sometimes it becomes difficult to analyze. Using machine learning techniques for this prediction and handling of data can become very efficient for medical people. Hence in this study, we have discussed the heart disease and its risk factors and explained machine learning techniques. Using that machine learning techniques, we have predicted heart disease and provided a comparative analysis of the algorithms for machine learning used for the experiment of the prediction. The goal or objective of this research is completely related to the prediction of heart disease via a machine learning technique and analysis of them.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Delhi Technological University, Shahbad Daulatpur, Delhi, -110042, India

    Rahul Katarya & Sunit Kumar Meena

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  1. Rahul Katarya
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  2. Sunit Kumar Meena
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Correspondence to Rahul Katarya.

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Katarya, R., Meena, S.K. Machine Learning Techniques for Heart Disease Prediction: A Comparative Study and Analysis. Health Technol. 11, 87–97 (2021). https://doi.org/10.1007/s12553-020-00505-7

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