Classification and Diagnosis of Heart Disease Using Machine Learning

Abstract

Heart disease is one of the most common causes of death worldwide, and accurate and rapid diagnosis is needed to avoid serious complications. This study aimed to present a system for diagnosing and classifying common heart diseases using machine learning techniques. The data used are a set of medical records for heart patients at Ibb Medical Clinic, Ibb City, Yemen. Fourteen different classification algorithms were implemented using the Weka tool to classify different heart diseases, including eight heart diseases. The results showed that the RF algorithm was the best in terms of accuracy, sensitivity, specification, and F-measure. Based on this algorithm, a web system based on the ASPX language was designed to help users enter patient data and obtain a diagnosis and classification of heart disease. The system can help doctors in the diagnosis process and increase the medical awareness of the community.