Bias Mitigation in Medical Datasets: Enhancing GBM and KNN Predictive Models with SMOTE Techniques

Dataset bias is a critical issue in medical machine learning models that can significantly impact the efficacy of disease prediction and diminish the ability to generate relevant clinical decisions. To address this problem, oversampling the underrepresented class presents a promising solution. This study investigates the impact of oversampling techniques, specifically SVM-SMOTE and BorderlineSMOTE, on both machine learning using K-Nearest Neighbors (KNN) algorithm and Ensemble Learning using Gradient Boosting Machine (GBM). we assessed changes in accuracy, precision, recall, F1 score, Positive Predictive Value (PPV), Equal Opportunity Difference (EOD), Disparate Impact (DI), and Imbalance Ratio (IR) across two medical datasets. In heart disease prediction using Gradient Boosting Machine (GBM), both SVM-SMOTE and BorderlineSMOTE significantly enhanced key metrics. BorderlineSMOTE maintained high accuracy at 0.85, with a notable increase in recall to 0.92., indicating improved identification of positive cases. Precision and F1 score also showed robust improvements, reflecting the models' enhanced ability to accurately classify instances across all classes. Also, the introduction of synthetic samples notably reduced EOD and DI values, indicating a reduction in bias within model predictions.