Abstract
In recent decades, heart disease, also known as cardiovascular disease, has emerged as the leading cause of death worldwide. It encompasses a range of conditions that affect the heart and is influenced by various risk factors. It has become increasingly imperative to develop accurate, dependable, and efficient methods for early diagnosis to facilitate timely disease management. To address this challenge, data mining has emerged as a valuable tool in the healthcare domain. Researchers have employed various data mining and machine learning techniques to analyze vast and complex medical datasets, aiding healthcare professionals in predicting the onset of heart disease. This research paper focuses on exploring different attributes associated with heart disease and building predictive models using supervised learning algorithms such as Naïve Bayes, decision trees, K-nearest neighbor, and the random forest algorithm. To conduct this analysis, an existing dataset from the Cleveland database o