A Hybrid Framework for Effective Chronic Diabetes Disease Management

Abstract

Diabetes is a serious chronic disease affecting millions of people in the world, and if not managed properly, can cause a lot of serious health problems. The challenges of traditional healthcare systems are unable to properly manage real-time patient monitoring, temporal healthcare analysis and accurate disease prediction. This study aims to solve the issues with a hybrid approach that combines Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks and ensemble learning techniques for effective management of chronic diabetes. The design includes the use of healthcare data from wearable sensors, Internet of Things (IoT) devices, and Electronic Health Records (EHRs) that will be used to measure glucose level fluctuations and forecast future health status. The healthcare data is preprocessed for better predictive accuracy and data quality through methods such as normalization, outlier elimination, and feature extraction. The ensemble mechanism fuses the advantages of both the RNN model and the LSTM model to boost the accuracy and robustness of the prediction results. The proposed method achieves an accuracy of 98%, precision of 92%, recall of 87%, F1-score of 92% and Mean Squared Error (MSE) value of 1 with better results than the traditional databases, rule-based system, statistical approaches and standalone machine learning models. The proposed framework is designed to facilitate real-time monitoring, personalized diabetes management recommendations and enhanced clinical decision making for chronic diabetes management.