DiabetesXpertNet: An innovative attention-based CNN for accurate type 2 diabetes prediction

Abstract

Type 2 diabetes mellitus remains a critical global health challenge, with rising incidence rates placing immense pressure on healthcare systems worldwide. This chronic metabolic disorder affects diverse populations, including the elderly and children, leading to severe complications. Early and accurate prediction is essential to mitigate these consequences, yet traditional models often struggle with challenges such as imbalanced datasets, high-dimensional data, missing values, and outliers, resulting in limited predictive performance and interpretability. This study introduces DiabetesXpertNet, an innovative deep learning framework designed to enhance the prediction of Type 2 diabetes mellitus. Unlike existing convolutional neural network models optimized for image data, which focus on generalized attention mechanisms, DiabetesXpertNet is specifically tailored for tabular medical data. It incorporates a convolutional neural network architecture with dynamic channel attention modules to prioritize clinically significant features, such as glucose and insulin levels, and a context-aware feature enhancer to capture complex sequential relationships within structured datasets. The model employs advanced preprocessing techniques, including mean imputation for missing values, median replacement for outliers, and feature selection through mutual information and LASSO regression, to improve dataset quality and computational efficiency. Additionally, a logistic regression-based class weighting strategy addresses class imbalance, enhancing model fairness. Evaluated on the PID dataset and Frankfurt Hospital, Germany Diabetes datasets, DiabetesXpertNet achieves an accuracy of 89.98%, AUC of 91.95%, precision of 89.08%, recall of 88.11%, and F1-score of 88.01%, outperforming existing machine learning and deep learning models. Compared to traditional machine learning approaches, it demonstrates significant improvements in precision (+5.1%), recall (+4.8%), F1-score (+5.1%), accuracy (+6.0%), and AUC (+4.5%). Against other convolutional neural network models, it shows meaningful gains in precision (+2.2%), recall (+1.1%), F1-score (+1.2%), accuracy (+1.9%), and AUC (+0.6%). These results underscore the robustness and interpretability of DiabetesXpertNet, making it a promising tool for early Type 2 diabetes diagnosis in clinical settings.