Abstract: This study develops a machine learning model for predicting heart disease risk using patient data, including demographics, medical history, and clinical measurements. Various algorithms such as Decision Trees, Support Vector Machines (SVM), and Neural Networks are evaluated for their predictive accuracy. The aim is to assist clinicians in early diagnosis and intervention. The model is evaluated using accuracy, precision, recall, and F1-score, and focuses on building a robust tool for heart disease prevention

Machine learning

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