A Comprehensive Analysis of a Framework for Rebalancing Imbalanced Medical Data Using an Ensemble-based Classifier

Abstract

In medical data, addressing imbalanced datasets is paramount for accurate predictive modeling. This paper delves into exploring a well-established rebalancing framework proposed in previous research. While acknowledged for its effectiveness, the adaptability of this framework across diverse medical datasets remains unexplored. We conduct a comprehensive investigation to bridge this gap by integrating an ensemble-based classifier into the existing framework. By leveraging seven imbalanced medical binary datasets, our study comprises three distinct experiments: utilizing standard baseline classifiers from the framework (original), incorporating the baseline with an ensemble-based classifier, and introducing our novel ensemble-based classifier with the self-paced ensemble (SPE) algorithm. Our novel ensemble, composed of decision tree (DT), radial support vector machine (R.SVM), and extreme gradient boosting (XGB) classifiers, serves as the foundation for the SPE. Our primary objective is to demonstrate the potential improvement of the existing framework’s overall performance through the integration of an ensemble. Experimental results reveal significant enhancements, with our proposed ensemble classifier outperforming the original by 4.96%, 5.89%, 5.68%, 7.85%, and 6.84% in terms of accuracy, precision, recall, F-score, and G-mean, respectively. This study contributes valuable insights into the adaptability and performance augmentation achievable through ensemble methods in addressing class imbalances within the medical domain.