Pragmatic Analysis in Supervised Machine Learning Methods for Heart Disease Risk Prediction

Heart disease has remained the leading cause of death globally over the previous two decades. In impoverished nations, inadequate resources and access to skilled medical personnel and diagnostic equipment make it difficult to properly diagnose and treat cardiac disease. One of the most important aspects of treating cardiac disorders is analyzing large datasets and using data to predict, prevent, and treat conditions such as heart attacks. It is typical for medical staff at the hospital to have differing degrees of training and experience when it comes to treating cardiac disease, which can have fatal consequences. The occurrence of heart disease can be predicted with the aid of several health parameter analysis. Various machine learning methods, like Decision Trees (DT), Naive Bayes (NB), K-Nearest Neighbors (KNN), and Logistic Regression (LR), can be utilized for the heart disease prediction. This research delves into cardiac disease prediction by employing different algorithms, parameters, and outcomes.