Heart Disease Prediction Using Machine Learning Algorithms

This chapter explores the application of machine learning (ML) techniques in predicting heart disease, a leading cause of mortality worldwide. Utilizing the Cleveland Heart Disease dataset, the chapter proposes a novel predictive model combining Decision Tree, Random Forest, and a Hybrid Model that integrates the strengths of both. The Hybrid Model achieves an impressive accuracy of 88.7%, outperforming individual algorithms. The chapter discusses the implementation of these techniques and highlights the development of a user-friendly interface that allows real-time prediction of heart disease risk based on user-provided parameters such as age, cholesterol, and blood pressure. Emphasizing the importance of early diagnosis, the findings demonstrate the potential of ML to enhance clinical decision-making and patient outcomes by enabling timely interventions. This chapter underscores the transformative role of ML in healthcare, particularly in leveraging data-driven approaches to address complex challenges like cardiovascular disease prediction.