HEART DISEASE PREDICTION USING MACHINE LEARNING CLASSIFIERS WITH VARIOUS BALANCING TECHNIQUES

Heart disease or Cardiovascular illness is the most prevalent cause of mortality globally. The challenge of predicting heart illness using clinical data analytics is considerable. Machine learning (ML) has been extensively used in the medical domain for disease prediction. This work performs a comparative analysis of various oversampling methods like the Synthetic Minority Oversampling technique (SMOTE), Synthetic Minority Oversampling Technique with Edited Nearest Neighbor (SMOTE-ENN), and Adaptive Synthetic Sampling Approach (ADASYN) Algorithm used with ML classifiers on imbalanced heart failure prediction dataset. Six ML classifiers are analyzed in the study Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). The accuracy metric is used to measure the model's performance The result depicts that the ADASYN technique performs better for the given dataset and increases the accuracy of the classifier in heart failure prediction.