A Case Study on Coronary Heart Disease using Machine Learning Techniques

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Ramanathan G.
Jagadeesha S. N.


Background/Purpose: We have seen an increase in coronary heart disease and heart attack risk in recent years. This is a case study on Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bengaluru to get a better understanding of the heart related ailments and their related symptoms. The hospital specializes in cardiology, cardiothoracic surgery and paediatric cardiology. Based on the symptoms various ailments are diagnosed and treated with different treatments like angioplasty, placement of stent, lifestyle changes and medicines. As part of the research, various health parameters will be collected and analyzed for diagnosing heart related ailments using Machine Learning methods. Determining the appropriate Machine Learning technique to achieve maximum accuracy is the key to achieve a better treatment and prevention of mortality.

Design/Methodology/Approach: This study was undertaken using secondary sources, such as website of Sri Jayadeva Institute of Cardiovascular Science and Research, journals, conference articles, the internet and scholarly articles. The SWOT framework is used to analyse, and present, the information acquired from web articles, scholarly papers and other sources.

Findings/Results: Heart ailments can be predicted using a few key parameters which can help in avoiding mortality. For this purpose machine learning algorthims, Neural Networks, Particle Swarm algorithm and many more can be applied on those medicial parameters.

Originality/Value: This paper reports an exhaustive and comprehensive overview of Coronary Heart Diseases and the treatment provided by Jayadeva Cardiology Hospital on different data collected.

Paper Type: Case study-based Research Analysis


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How to Cite
Ramanathan G., & Jagadeesha S. N. (2022). A Case Study on Coronary Heart Disease using Machine Learning Techniques. International Journal of Health Sciences and Pharmacy (IJHSP), 6(2), 149–165. https://doi.org/10.47992/IJHSP.2581.6411.0091