Machine Learning-Based Predictive Healthcare System for Early Heart Disease Detection

Heart disease is one of the leading causes of death across the world, making early diagnosis and prediction extremely important in modern healthcare systems. Traditional medical diagnosis methods often require continuous monitoring, laboratory testing, and expert analysis, which may consume considerable time and resources. With the rapid growth of Artificial Intelligence and Machine Learning technologies, intelligent healthcare systems are becoming more effective in supporting medical professionals during disease prediction and diagnosis. This research presents a Machine Learning-based Predictive Healthcare System for early heart disease detection using patient healthcare data. The proposed system uses different machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine to analyze medical parameters such as age, blood pressure, cholesterol level, heart rate, fasting blood sugar, and chest pain type. The primary objective of the system is to improve prediction accuracy and assist healthcare professionals in making faster and more reliable decisions. The proposed methodology includes data preprocessing, feature selection, model training, testing, and performance evaluation using the UCI Heart Disease Dataset. Experimental analysis demonstrates that Machine Learning techniques can significantly improve healthcare prediction systems. Among all implemented algorithms, Random Forest achieved the highest prediction accuracy. The proposed system can help reduce human error, support early diagnosis, and contribute toward intelligent healthcare solutions.