Prediction of Cardiac Disease Using Naive Bayes Algorithm

The goal of this work is to use machine learning methods, such as the Naive Bayes algorithm, to accurately forecast cardiac disease. The proposed work implements K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Naïve Bayes to perform regression and classification analysis on the data to attain improved accuracy. This revised dataset is used to train the models, and standard metrics are used to thoroughly assess the model's predicted performance. With an accuracy of 88%, the naive bayes algorithm performed better than the KNN algorithm. In the context of forecasting cardiac disorders, this conversation offers a thorough review of the Naive Bayes method. The implementation generates accurate predictions based on crucial health factors through data preprocessing, feature selection and model training.