Heart Disease Prediction Using Machine Learning

Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning- based technique is needed to predict heart failure health status early and take necessary actions to overcome this worldwide issue. The study addresses data preparation difficulties such as missing values and outliers by leveraging a large dataset including critical health parameters such as age, gender, blood pressure, and cholesterol levels. The models are trained using this new dataset, and the anticipated performance is rigorously assessed using standard criteria. The study includes a detailed review of the Warm and Naive Bayes (NB) models, highlighting their benefits and drawbacks in the context of heart disease prediction. Our proposed research study has significant scientific contributions to the medical community.