A Stacking Ensemble Classifier with GAN-SFLA for Improved Diagnosis in Imbalanced Healthcare Data

Abstract

The widespread issue of data imbalance in healthcare classification tasks presents a significant challenge, as minority class instances, often representing critical yet less firequent medical conditions such as rare diseases, are overshadowed by the majority class. This imbalance can lead to poor predictive performance in medical diagnostics, where accurately identifying rare but severe conditions is crucial for patient outcomes. To address this, this study introduces a stacking ensemble classifier based on a Generative Adversarial Network (GAN), enhanced by the Shufed Frog Leaping Algorithm (SFLA). The core innovation of this approach lies in the utilization of SFLA to optimize the GAN's generative process, producing highly representative synthetic instances of the minority class. These synthetic instances enhance the authenticity of rare disease cases, which are often underrepresented in clinical datasets. By refining these instances through iterative interactions with the stacking ensemble, the GAN adapts them to closely resemble misclassified samples, thereby improving the system's diagnostic accuracy. This adaptive integration of SFLA and GANs results in a more robust ensemble classifier specifically tailored to handle the complexities of medical data. The technique was tested on imbalanced medical datasets, including those for thyroid and skin disorders, yielding an F1-score and AUC between 0.8 and 1. It outperformed the compared algorithms by 10-15%, significantly enhancing the accuracy of healthcare system evaluations, particularly for rare conditions.