Comparative Performance Assessment Of Machine Learning Algorithms To Predict Cardiovascular Disease

Cardio Vascular Disease (CVD) are a major factor in many deaths across the world. Many people in their middle or later years suffer from heart conditions, which typically result in significant health problems include heart attacks and strokes.In order to stop any significant health problems before they arise, it is vital to detect and anticipate cardiac illnesses. In order to predict cardiovascular diseases at various evaluation stages, this paper used a variety of machine learning approaches, including Neural Networks (NN), Decision Trees (DT), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and XG Boost. Results reveal that the Random Forest approach performed better than the other methods and had a predicted accuracy rating of 98.25%.