Authors : Meghna Das; Koushik Karmakar

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/5556uee4

DOI : https://doi.org/10.38124/ijisrt/24may986

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : One of the most important issues facing worldwide today is the problem of cardiac disease prediction. One of the major challenges in the field of clinical data analysis is the prediction of cardiovascular disease. Heart disease instances are rising quickly every day, therefore it's critical to identify any potential risks in advance. In this study, we have suggested a cardiac disease prediction system that lowers costs and improves medical treatment. We get important information from this experiment that will aid in the prediction of heart disease patients. Making judgements and forecasts from the vast amounts of data generated by hospitals and the healthcare sector has proven to be aided by hybrid machine learning (ML). Our suggested approach will perform better and get accurate results.

Keywords : Healthcare, Cardiac Disease Problem, Machine Learning.

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