Deep Learning-Based Framework for Rapid Screening for Type 2 Diabetes Onset in Healthcare Applications

Type 2 Diabetes Mellitus (T2DM) is a widespread chronic metabolic disease, which has become a most important health and financial burden, and that is why there is imperative to have effective early diagnostic options. The explosion of diverse healthcare data provides new vistas of predictive modeling, but the traditional approaches to diagnostics and the traditional methods of machine learning are still confined in terms of their ability to identify nonlinear and complicated relations. The paper introduces a deep learning-based framework of early T2DM detection through the combination of advanced neural designs and data preprocessing and interpretation. Various models are tested such as ensemble classifiers, Convolutional neural networks (CNNs), Autoencoders (AEs), and a Temporal Inception Perceptron Network (TIPNet) based on various clinical, demographic, and lifestyle characteristics. To deal with the problem of class imbalance, adaptative synthetic oversampling algorithms are used, whereas explainable artificial intelligence tools like LIME and SHAP are used to improve the transparency of the model. The experimental findings of independent testing and 10-fold cross-validation show that the proposed framework can have better predictive performance, and its accuracy and significant enhancement of F1-score, recall, and AUC. The results support the framework effectiveness as a smart clinical decision-support instrument in terms of early diagnosis and individual care.