Enhanced Multitask Learning with Attention Sparse Routing Mechanism for Intelligent Diabetes Prediction

Le résumé fourni par la source

With the rapid advancement in machine learning and artificial intelligence, their application in the medical field has gained increasing prominence, particularly for the early detection and personalized treatment of chronic diseases. This study presents an intelligent approach for early diabetes recognition, based on an encoder–decoder structure and an enhanced multitask learning model. A multilayer self‐attention mechanism is employed to automatically and efficiently extract feature representations, eliminating the limitations of traditional manual feature selection while improving the model's adaptability to complex data. Furthermore, by integrating an enhanced expert module with a sparse routing mechanism, each expert is treated as a weak classifier, leveraging dropout to reduce the network parameters and mitigate overfitting. The sparse routing mechanism dynamically allocates samples to the optimal expert network, achieving a balance between computational efficiency and model accuracy. Experiments are conducted using the TIANCHI precision medicine competition and NHANES data sets. The results demonstrate that this approach significantly improves diabetes risk prediction compared to various baseline models.

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