FLTGAN: A Novel Framework for Enhanced Diabetes Classification in Imbalanced Datasets

Abstract

Class imbalances in diabetes datasets are common in datasets in the medical research field, and class imbalances may cause the training effects of machine learning models to be biased toward a larger number of classes, thus affecting the model's ability to predict a small number of classes. In order to solve this problem, this study designed and implemented a diabetes classification model based on Focal Loss Tabular Generative Adversarial Network (FLTGAN) to effectively solve the problem of sample imbalance in uneven datasets. The model, integrating advanced machine learning algorithms, has been validated using two real-world datasets. Comparative results demonstrated that our model significantly outperformed traditional TGAN and SMOTE methods across multiple evaluation metrics, it achieved an F1 score of 0.78, an AUC of 0.84, an accuracy of 0.80, a precision of 0.83, and a recall of 0.75, indicating its superior classification performance. This study confirmed the robust capability of the proposed intelligent diabetes classification method in accurately diagnosing diabetes and addressing class imbalance, offering a new effective tool for the intelligent diagnosis of diabetes.