Resampling Imbalanced Healthcare Data for Predictive Modelling

<p dir="ltr">Imbalanced datasets pose significant challenges in healthcare for developing accurate predictive models in medical diagnostics. In this work, we explore the effectiveness of combining resampling methods with machine learning algorithms to enhance prediction accuracy for imbalanced heart and lung disease datasets. Specifically, we integrate undersampling techniques such as Edited Nearest Neighbours (ENN) and Instance Hardness Threshold (IHT) with oversampling methods like Random Oversampling (RO), Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). These resampling strategies are paired with classifiers including Decision Trees (DT), Random Forests (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM). Model performance is evaluated using accuracy, precision, recall, F1 score, and the Area Under the Curve (AUC). Our results show that tailored resampling significantly boosts machine learning model performance in healthcare settings. Notably, SVM with ENN undersampling markedly improves accuracy for lung cancer predictions, while SVM and RF with IHT achieve higher validation accuracies for both diseases. Random oversampling shows variable effectiveness across datasets, whereas SMOTE and ADASYN consistently enhance accuracy. This study underscores the value of integrating strategic resampling with machine learning to improve predictive reliability for imbalanced healthcare data.</p>