• T.Abhiram
• K.V.Yaswanth
• M.Sai Swetha
• K.Srujini Devi
• Dr.S.K.Raghu

Among the biggest causes of death worldwide are coronary artery disease. One person globally passes away from coronary heart disease every minute. For many centuries, it has been the main cause of death in affluent, undeveloped, and developing nations alike. Mortality can be decreased with early detection of myocardial infarction and extensive medical monitoring. While it takes more knowledge, time, and experience, careful daily patient monitoring is not always possible, and a doctor is not always available to confer with patients. In this study, leveraging machine learning methods including backward elimination algorithms, logistic regression, and using a dataset that is openly accessible in Kaggle, we develop and test models to predict heart illness based on numerous parameters of a patient's heart and detect imminent heart disease. An essential medical step would be to assist at-risk people in changing their lifestyles and reducing complications by early detection of cardiovascular disease.

KNN algorithm, Random Forest algorithm, ML (Machine Learning), Cardiovascular Disease, Heart Dataset, Cardiovascular disease prediction system, Heatmap, Correlation, Feature Engineering, Cardio care.

"CARDIOCARE – A CARDIOVASCULAR DISEASE PREDICTION SYSTEM BASED ON KNN AND RANDOM FOREST ML ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.e405-e410, March-2023, Available :http://www.jetir.org/papers/JETIR2303449.pdf

"CARDIOCARE – A CARDIOVASCULAR DISEASE PREDICTION SYSTEM BASED ON KNN AND RANDOM FOREST ML ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppe405-e410, March-2023, Available at : http://www.jetir.org/papers/JETIR2303449.pdf