Scholarlink

<p>Accurate diabetes risk prediction is essential for timely intervention and effective disease management. To address these issues, this study evaluates a prediction framework that incorporates Conditional Tabular Generative Adversarial Network (CTGAN) to generate additional synthetic samples and mitigate class imbalance. Unlike interpolation-based oversampling techniques, CTGAN models the underlying data distribution and may better preserve nonlinear relationships among body composition variables. When combined with a Multilayer Perceptron (MLP), this approach enables the model to capture complex feature interactions that could be relevant for distinguishing individuals with diabetes from healthy participants. In our experiments, the CTGAN-augmented MLP achieved an accuracy of 93.91%, an AUC of 93.87%, a precision of 94.48%, and an F1-score of 93.89% under stratified 5-fold cross-validation, representing the highest performance among the evaluated models. The SHapley Additive exPlanations (SHAP) analysis was further employed to enhance interpretability and provided insight into the contribution of key predictors such as fat percentage, fat-free mass, and basal metabolic rate.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced diabetes prediction using CTGAN-MLP approach on body composition data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月10日

查看全文
  • Javad Hassannataj Joloudari,
  • Mohammad Maftoun,
  • Mohammad Ali Nematollahi,
  • Kandala N. V. P. S. Rajesh,
  • S. Prasanth Vaidya,
  • Kamireddy Rasool Reddy,
  • Pirhossein Kolivand

摘要

Accurate diabetes risk prediction is essential for timely intervention and effective disease management. To address these issues, this study evaluates a prediction framework that incorporates Conditional Tabular Generative Adversarial Network (CTGAN) to generate additional synthetic samples and mitigate class imbalance. Unlike interpolation-based oversampling techniques, CTGAN models the underlying data distribution and may better preserve nonlinear relationships among body composition variables. When combined with a Multilayer Perceptron (MLP), this approach enables the model to capture complex feature interactions that could be relevant for distinguishing individuals with diabetes from healthy participants. In our experiments, the CTGAN-augmented MLP achieved an accuracy of 93.91%, an AUC of 93.87%, a precision of 94.48%, and an F1-score of 93.89% under stratified 5-fold cross-validation, representing the highest performance among the evaluated models. The SHapley Additive exPlanations (SHAP) analysis was further employed to enhance interpretability and provided insight into the contribution of key predictors such as fat percentage, fat-free mass, and basal metabolic rate.