Hybrid Feature Optimization and Attention-Enhanced Siamese Deep Learning for Large-Scale Diabetes Prediction
DOI:
https://doi.org/10.31838/NJAP/08.02.21Keywords:
Diabetes prediction; Feature selection; Siamese network; 1D-CNN; Deep learning; Electronic health records; Medical decision support.Abstract
Diabetes mellitus is an alarming issue that is spreading across the world and requires proper and early prediction of the risk to aid in timely clinical intervention. This paper will suggest a small and streamlined diabetes prediction model that incorporates a hybrid IGF–DMO–RFO feature-selection approach with a Siamese one-dimensional convolutional neural network optimized by Global Spatial-Channel Attention (GSCA). The model is tested on a large scale public diabetes data of 100,000 patients records with demographic, lifestyle, comorbidity and metabolic features. The hybrid feature-selection pipeline identifies a concise and highly informative subset of predictors, while the Siamese architecture employs contrastive learning to generate discriminative embeddings from structured clinical data. Experimental results demonstrate that the proposed framework achieves an overall classification accuracy of 97%, with a ROC-AUC of 0.9726 and reliable probability calibration, outperforming conventional machine-learning and single-branch deep-learning baselines. The lightweight architecture enables fast inference and robustness to class imbalance, making it suitable for large-scale diabetes screening and clinical decision-support applications.
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