A Machine Learning Model for Early Prediction of Heart Diseases
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Abstract
Heart disease is a major global health concern and remains one of the leading causes of death worldwide. Early prediction and diagnosis are essential to prevent severe complications and improve patient survival rates. This project proposes a machine learning-based model for the early prediction of heart diseases using patient clinical and lifestyle parameters. The system analyses feature such as blood pressure, cholesterol level, age, chest pain type, and other medical indicators to classify the risk of heart disease. Various machine learning algorithms including Support Vector Machine(SVM),Logistic Regression, and Random Forest were implemented and evaluated using performance metrics such as accuracy, precision, recall and F1-score,Among these, the SVM model demonstrated superior performance and was selected as the final predictive model. The trained model was integrated into Flask-based web application to provide real -time predictions through a user- friendly interface The proposed system supports early detection, assists healthcare professionals in decision making, and reduce dependency on manual diagnosis. As a future enhancement, the system is planned to be extended into multi-disease prediction framework by incorporating models for chronic-kidney disease and liver disease, enabling a comprehensive and intelligent healthcare screening platform.
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