An explainable hybrid deep learning model for prediabetes prediction in men aged 30 and above

Abstract

Men diagnosed with type 2 diabetes at age 30 have a significantly reduced life expectancy compared to their non-diabetic peers. Prediabetes, like diabetes, not only heightens the risk of microvascular complications but also significantly increases the likelihood of cardiovascular diseases and overall mortality. Early detection and intervention in prediabetes can prevent these complications and delay or avoid the progression to diabetes. However, prediabetes prediction remains a challenging area due to biased accuracy and a lack of explainability in many existing machine learning methods. To address these issues, this study aims to develop a novel hybrid model, HyperTab-LIME, which combines a hypernetwork-based deep learning approach designed for small tabular datasets (HyperTab) with the Local Interpretable Model-Agnostic Explanations (LIME) framework to predict prediabetes in men aged over 30. We employed data from 1527 male participants aged 30 and above from the 2022 Korean National Health and Nutrition Examination Survey to train and test our model. The performance of HyperTab-LIME was evaluated against several baseline models. The results demonstrated that the HyperTab-LIME model excelled, achieving the Area Under the Receiver Operating Characteristic Curve (AUC) scores of 0.8429 on the validation set and 0.8300 on the hold-out set, thereby outperforming other baseline models included in this study. To provide a dependable and interpretable diagnostic tool for healthcare professionals, the optimized HyperTab model was integrated with the LIME framework. This innovative approach ensures detailed and precise explanations of predictive outcomes, greatly enhancing the medical community’s ability to understand and trust the model’s decisions, thereby facilitating earlier interventions in the treatment of prediabetes.

Keywords

• explainable ai
• hypertab
• lime
• prediabetes