Review on Machine Learning Techniques for Heart Failure Analysis in Health Industries

Year : 2024 | Volume :13 | Issue : 01 | Page : 29-43

Amit Arya

Vineet Richhariya

Sadhna K. Mishra

  1. Research Scholar Lakshmi Narain College of Technology M. P India
  2. Professor Lakshmi Narain College of Technology M. P India
  3. HOD Lakshmi Narain College of Technology M. P India


There are few bodily components as crucial as the heart. It aids in the filtration and distribution of blood to every area of a body. The world’s biggest cause of death is heart disease. It has been reported that symptoms include breathing difficulties, fast heartbeat, and chest discomfort. They analyze this data on a regular basis. This review begins with a brief introduction of cardiac disease and the present methods used to treat it. It also provides a concise overview of the most important ML methods currently published for the forecasting of CVD. Data analytics is helpful for making predictions with more data, and it aids the medical Center in forecasting a variety of ailments. The monthly data retention rate is quite high. The collected information may serve as a foundation for disease outbreak prediction. Predictions and judgements have become feasible because to the massive amounts of data produced by the healthcare business. Cardiovascular disease prediction and prevention is the greatest data analytic problem. The abundance of data generated by healthcare facilities has prompted the development of machine learning algorithms that can make accurate forecasts and sound decisions. The area of Machine Learning (ML) within AI focuses on teaching computers new skills and tasks with little to no human oversight. Finding patterns and making predictions are the goals of data analysis and statistical approaches. This research compared many Machine Learning models to find the most effective one for making more accurate predictions of cardiovascular disease (CVD). Finally, the survey delves into several research gaps and difficulties, providing researchers with valuable information to inspire better future work on HD prediction using ML models.

Keywords: Heart failure, Cardiovascular, Risk factor, Machine learning, Cardiovascular Disease.

[This article belongs to Research & Reviews : Journal of Medical Science and Technology(rrjomst)]

How to cite this article: Amit Arya, Vineet Richhariya, Sadhna K. Mishra. Review on Machine Learning Techniques for Heart Failure Analysis in Health Industries. Research & Reviews : Journal of Medical Science and Technology. 2024; 13(01):29-43.
How to cite this URL: Amit Arya, Vineet Richhariya, Sadhna K. Mishra. Review on Machine Learning Techniques for Heart Failure Analysis in Health Industries. Research & Reviews : Journal of Medical Science and Technology. 2024; 13(01):29-43. Available from:


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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received February 23, 2024
Accepted February 29, 2024
Published April 16, 2024