Machine Learning Models for Heart Attack Prediction
This research investigates the application of various machine learning models for predicting heart attack risk, contributing to early diagnosis and preventive healthcare systems. The study explores different algorithms and techniques to analyze cardiovascular risk factors and develop predictive models that can assist healthcare professionals in identifying patients at high risk of heart attacks. The proposed methodology demonstrates the potential of machine learning in cardiovascular medicine and preventive care applications.
Machine Learning Models for Heart Attack Prediction
This research investigates the application of various machine learning models for predicting heart attack risk, contributing to early diagnosis and preventive healthcare systems. The study explores different algorithms and techniques to analyze cardiovascular risk factors and develop predictive models that can assist healthcare professionals in identifying patients at high risk of heart attacks. The proposed methodology demonstrates the potential of machine learning in cardiovascular medicine and preventive care applications.
Key Highlights
- Application of various ML models for predicting heart attack risk
- Contributing to early diagnosis and preventive healthcare systems
- Analyze cardiovascular risk factors and develop predictive models
- Assist healthcare professionals in identifying high-risk patients
Technologies Used
- Heart Attack Prediction - Cardiovascular risk assessment
- Machine Learning - Predictive modeling algorithms
- Cardiovascular Medicine - Heart disease analysis and treatment
- Healthcare AI - Artificial intelligence in medical applications
- Predictive Analytics - Risk prediction and forecasting
- Medical Diagnosis - Disease identification and assessment
- Preventive Healthcare - Early intervention and prevention
- Risk Assessment - Patient risk evaluation systems
Read the full research paper on ResearchGate!