Jummy David’s Capstone Presentation:A Comparison of Different ML Models for Predicting Heart Attacks
Jummy’s capstone presentation examines how different machine learning models perform when you have a patient showing symptoms/signs associated with a heart attack, correctly predicting the chance of the disease with a zero error and 100% accuracy becomes an issue. For accurate prediction, early detection, and management of heart attack in the medical field, we address this problem by comparing different machine learning models’ performance and accuracy. This comparison was based on model accuracy, R2 scores, room mean square error (RMSE) and confusion matrix using the Scikit-Learn libraries in Python. We found that the Support Vector Machine (SVM) performed best and conclude that SVM should be used for diagnosing a disease such as heart attack in the medical
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