Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals
Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Raed Alazaidah
Abstract
Heart disease is the leading cause of mortality worldwide. Early identification and prediction can play a crucial role in preventing and treating it. Based on patient data, machine learning techniques may be used to construct cardiac disease prediction models. This work aims to investigate the usage of machine learning models for heart disease prediction utilizing a publicly available dataset. The dataset contains patient information on clinical and demographic characteristics and the presence or absence of cardiac disease. Based on classification performance, many machine learning methods were tested and compared. The findings reveal that machine learning models can predict cardiac disease with accuracy and AUC values. Furthermore, the developed system is used to examine some Jordanian patients, and the predictions of the results are satisfactory. The study's findings might have far-reaching consequences for the early identification and prevention of heart disease, as well as for improving patient outcomes and lowering healthcare expenditures.