Cardiovascular Disease Prediction: Performance Analysis and Comparison of Various Supervised Machine Learning Algorithms

Cardiovascular disease also termed as heart disease includes many types of disorders related to heart. Heart disease kills more people annually than all other diseases combined, making it the leading cause of morbidity and mortality worldwide. The prevalence of heart disease is increasing at an alarming rate, making early illness prediction essential. The main objective of this research is to investigate the most suitable machine learning algorithm for heart disease prediction. Hospitals may now easily do automatic diagnoses since they are playing a crucial part in resolving these issues with heart disease forecasts thanks to data science algorithms and data mining approaches. This research paper is based on supervised learning methods like Logistic Regression (LR) and Gaussian Na�ve Bayes (NB). This work also explores the applicability of other methods such as K-Nearest Neighbours (KNN), Decision Trees (DT) and Random Forest (RF). It displays numerous heart disease-related features. The findings show that accuracy produced by the Random Forest is 92% which is better than other models.