Multi-Task Learning Approach for Simultaneous Prediction of Multiple Comorbidities in Diabetic Patients

The proposed work gives a Multi-Task Learning (MTL) mechanism for the simultaneous predictions of multiple comorbidities in diabetic patients, aiming to improve early detection and personalized healthcare treatment strategies. By leveraging MTL, the proposed model will train to predict multiple comorbid conditions such as cardiovascular-related disease, kidney-affecting disease, and even neuropathy in a single unified methodology. The model uses shared representations across tasks while allowing for task-specific metrics, enhancing prediction accuracy by learning the interdependencies between comorbidities. A Supervised learning algorithm like a deep neural structured network (DNN) is used, where the input features include the patient's demographical type data, past medical history, laboratorybased results, and clinically given measurements. The model is trained using a vast dataset of diabetic patients, and the multitask learning schema is optimized through backpropagation. Performance Evaluation metrics like sugar levels, BP analysis, and heartbeat rate are utilized to assess the model's performance. The results showcase the effectiveness of the MTL mechanism in capturing the underlying relationships between comorbidities, outperforming single-task models in both prediction accuracy rate and computational efficacy. This framework can potentially assist healthcare providers in delivering a more comprehensive way of care for diabetic patients with multiple comorbidities.