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Prediction of heart attack using machine learning

Kumar, Manoj
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2025
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Poster
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Quest
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Quest2025_041.pdf
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http://hdl.handle.net/20.500.12648/16599
Abstract
This study employs machine learning models to predict heart attack risk using clinical data, including blood pressure, cholesterol, and lifestyle factors. Algorithms like Logistic Regression, Random Forest, and XGBoost were evaluated, achieving 85.6% accuracy and a 90.3% AUC ROC score. This approach enhances early detection, aiding healthcare professionals in clinical decision-making.
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