Enhancing Cardiovascular Health Through Machine Learning

Abstract

Mohammad Salman Khan and Mahrukh

Heart disease remains a leading cause of mortality worldwide, emphasizing the need for early and accurate prediction to improve patient outcomes. This study explores the application of Artificial Intelligence (AI) in predicting the risk of heart disease using machine learning algorithms. By analyzing patient data, including age, cholesterol levels, blood pressure, and lifestyle factors, AI models can identify patterns and generate risk assessments. Using the publicly available Cleveland Heart Disease Dataset, we trained and evaluated machine learning models such as Logistic Regression, Random Forest, and Neural Networks. The Random Forest model achieved an accuracy of 89%, highlighting its potential for reliable prediction.

This research also examines the importance of key features in disease prediction, such as cholesterol and resting blood pressure, and discusses the challenges of data quality, model interpretability, and ethical considerations in deploying AI for healthcare. The results demonstrate that AI offers a scalable and cost-effective solution for early detection and personalized risk assessment of heart disease, paving the way for smarter, data-driven decision-making in cardiology.

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