A DATA-DRIVEN APPROACH TO HEART ATTACK RISK PREDICTION USING MACHINE LEARNING ALGORITHMS

  • Unique Paper ID: 168382
  • Volume: 11
  • Issue: 5
  • PageNo: 2267-2276
  • Abstract:
  • Heart disease is a significant cause of global mortality, with heart attacks being a critical concern in public health. Early prediction of heart attack risk allows for timely intervention and preventive measures, potentially reducing fatality rates. This study investigates the application of machine learning (ML) techniques to predict heart attack risk using patient medical records and lifestyle data. We collect and preprocess data from various sources, including demographic and medical attributes like age, cholesterol levels, blood pressure, and more. Several ML models are trained and evaluated for their prediction capabilities. The models are assessed using performance metrics such as accuracy, precision, recall, and AUC-ROC. Our results demonstrate that machine learning can provide accurate predictions of heart attack risk, highlighting key features that contribute to the risk. This research emphasizes the potential of ML in healthcare for improving early diagnosis and promoting personalized preventive strategies.

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 2267-2276

A DATA-DRIVEN APPROACH TO HEART ATTACK RISK PREDICTION USING MACHINE LEARNING ALGORITHMS

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