Enhanced Diabetes Prediction Using Deep Autoencoder Framework and Electronic Health Records

Type II diabetes, which nowadays has become one of the most common chronic metabolic illnesses, needs cloud-based information systems with great predictive models to ensure timely intervention and proper management. Given the fact that EHRs and deep learning are quite advanced serves as a solution to the given problem. The study is about the application of the improved diabetes prediction model that makes use of prevailing deep learning architecture and EHR data. The framework constitutes a three-step process: It encapsulates a preprocessing step, a feature extraction stage, and a training procedure for a deep autoencoders that learns levels of representation. Demonstration of the system's effectiveness becomes evident when it shows its improved ability of prediction compared to baseline models which in turn outperform in basic indicators such as accuracy, precision, recall, F1 score, and AUC-ROC. The result of the sensitivity check demonstrates that the model is able to perform well even under different hyperparameter arrangements. Latent feature analysis shows some unequivocal ways of hidings that reflect diabetes risk factors thus making it easy to understand. The clinical validation in a healthcare setting presents high accuracy, the specificity and the predictive values that are known in indicating those who are at the risk of diabetes onset. This research aims at deepening the understanding for the diagnosis of diabetes through the implementation of deep learning methods and generating real-world medical data. It attempts to provide better prognosis, lower healthcare expenditures and alleviate diabetes burden on patients and society.