Predicting Heart Disease with Machine Learning: Application of Logistic Regression in E-Healthcare Systems

Abstract

The increasing prevalence of heart disease necessitates efficient predictive models to aid in early diagnosis and intervention. This research paper explores the application of logistic regression, a fundamental machine learning technique, in predicting heart disease within electronic healthcare (E-healthcare) systems. We analyze various clinical and demographic features that contribute to heart disease and employ logistic regression to develop a predictive model. The findings demonstrate the potential of machine learning, particularly logistic regression, in enhancing decision-making processes in E-healthcare environments, ultimately leading to improved patient outcomes.