Authors : Vidyashree K; Thippeswamy K

Volume/Issue : Volume 7 - 2022, Issue 8 - August

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3wiuljj

DOI : https://doi.org/10.5281/zenodo.7011698

Abstract : Cardiovascular diseases, normally address all kinds of diseases associated with the heart and is being treated globally as the main cause of mortality. Numerous risks are associated with heart diseases, a need of the hour is to get worth, reasonable and accurate early diagnosis method and treatment. Machine Learning is being used in many areas to solve such problems. The aim of this project is to predict the heart disease in individuals. The use of many classification algorithms in machine learning (ML) on standard dataset has revealed that there is a need to improve the accuracy as taking risk in heart related diseases is not acceptable. The findings show that highest accuracy is achieved through Decision Tress Classifier (93%) and combining more than one method is being used on same dataset in all possible combinations and achieved an accuracy of 98.1% keeping in mind, the time complexity of final algorithm

Keywords : Supervised Learning, Classifier, Cardio Vascular Disease, Classificiation, Confusion Matrix.