Logistic Regression for Early Heart Disease Detection: Improving E-Healthcare through Machine Learning

Abstract

Heart disease remains one of the leading causes of mortality worldwide. Early detection is critical in improving patient outcomes and reducing healthcare costs. In this paper, we explore the application of logistic regression in predicting heart disease risk, leveraging patient data such as blood pressure, cholesterol levels, and lifestyle factors. Logistic regression is a powerful yet interpretable machine learning model well-suited for binary classification tasks, such as identifying whether a patient is at risk of developing heart disease. The aim of this research is to demonstrate how logistic regression can be effectively implemented in e-healthcare systems to enhance early diagnosis, improve personalized treatment plans, and ultimately reduce mortality rates.