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Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.

Python 98.50% R 1.50%
adaboost balancing borderline-smote decision-trees feature-selection forward-selection logistic-regression machine-learning naive-bayes-classifier python

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