Predictive Analytics for Heart Diseases Using Machine Learning

The diagnosis and prediction of cardiovascular illness are critical medical responsibilities that assist cardiologists correctly classify patients and treat them accordingly. Since ML algorithms are here to identify a similar pattern in the patient data. Misdiagnosis in diagnosis of CV diseases is also decreased by the doctorsusing ML Patterns. In order to bring down the death rate from cardiovascular disorders. In the creation of this paper the used model can accurately forecast the CVDs conditions. This paper suggests a Fresh beginning k-modes clustering approach that can increase classification accuracy. There are several models that are employed, including DT, RFClassifier, K-Nearest Neighbor, SVM etc., was utilized to hyper tune employed ML model's parameters. On real-world dataset of 300+ instances from Kaggle, the suggested model is used.