The Design of a Deep Learning-based Adaptive Multi-Channel Fusion Network for Diabetes Diagnosis

Accurate diagnosis of diabetes is crucial for effective health management of patients. Recent advances in machine learning have shown promising predictive results in diabetes diagnosis. In this paper, we developed an Adaptive Multi-Channel Fusion Network AMCFN . Specifically, we defined a feature enhancement module that combines attention mechanisms to adaptively enhance input data. Meanwhile, we designed a multi-channel fusion network capable of simultaneously extracting various deep features, including temporal and nonlinear features, from the input data. Extensive experiments were conducted on the Pima Indian Diabetes Dataset PIDD and the Early-Stage Diabetes Risk Prediction Dataset ESDRPD . Our model achieved high predictive accuracies of 95.83 and 99.6 , respectively. These results outperformed existing baseline models in diabetes diagnosis. Ablation experiments emphasized the power of the feature enhancement module and the multi-channel fusion network. Finally, we analyzed the prediction process of AMCFN using SHapley Additive exPlanations SHAP . The analysis results show the importance ranking of each feature to the model output in different channels, and the importance ranking of each channel to the final diabetes diagnosis. This enhances the interpretability of AMCFN and validates the effectiveness of the multi-channel design. Our model demonstrates potential in diabetes diagnosis and is expected to increase end-user trust and confidence in early detection of diabetes.