Published Paper Details:

Heart disease remains one of the leading causes of mortality worldwide, necessitating early detection and accurate prediction to improve patient outcomes. Traditional diagnostic methods often require extensive medical expertise and expensive tests, leading to delays in diagnosis. Machine learning (ML) techniques offer a promising solution by analyzing vast datasets to identify patterns indicative of heart disease. This study explores the application of various ML algorithms, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines, to predict heart disease based on clinical parameters such as blood pressure, cholesterol levels, and lifestyle factors. The dataset used for this research is sourced from reputable medical repositories, ensuring reliability and robustness. The proposed model achieves high accuracy in disease classification, showcasing the effectiveness of ML-driven predictions. The findings highlight the potential of machine learning in aiding healthcare professionals with early detection, improving treatment plans, and reducing death rates. (CVDs) account for approximately 17.9 million deaths each year, making them the leading cause of mortality worldwide. Early detection and prevention are crucial in reducing the fatality rate and improving patient survival. Conventional diagnostic approaches, such as electrocardiograms (ECG), echocardiograms, and angiograms, require specialized equipment and expertise, making them less accessible in many regions. In recent years, machine learning has emerged as a powerful tool in the healthcare domain, capable of analyzing complex medical data to identify patterns and predict diseases with high accuracy. By leveraging ML algorithms, healthcare professionals can improve diagnostic precision, automate risk assessment, and provide personalized treatment recommendations. This study focuses on applying various machine learning techniques to predict heart disease using clinical datasets. The primary objective is to develop a model that can accurately classify individuals at risk of heart disease based on key health indicators, including age, cholesterol levels, blood pressure, diabetes status, and lifestyle habits.