The role of deep learning in predicting disease progression in diabetic patients

The worldwide healthcare system confronts a substantial challenge from diabetes mellitus, which requires creative methods to handle the disease. Prolonged monitoring with deep learning techniques enables healthcare professionals to detect complicated medical patterns which aid in preliminary patient diagnosis and response. This research adopts Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Hybrid Neural Network integrated with Genetic Algorithm (Hybrid NN + GA) as deep learning structural networks for diabetic disease prognostication. The Diabetes 130-US Hospitals dataset served as the foundation for trainer and test protocols for the model while containing extensive clinical electronic health records about diverse patient information. AUC value assessment together with RMSE tested the models through accuracy evaluations. There is evidence that the hybrid approach excels at generalization in medical settings by surpassing both LSTM and GRU models when analyzing imbalanced hospital records. The research proves that joint utilization of deep learning frameworks produces better clinical decision systems for precise diabetes monitoring that enhances operational efficiency.