IoT-Aware Real-Time Healthcare Diagnostic Framework for Diabetes Using Wearable Sensors Through Deep Reinforcement Learning

machine learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this article, a 5G aware real-time diabetes prediction framework is proposed using optimized bidirectional long short-term memory (Bi-LSTM) with deep reinforcement learning (DRL). Bi-LSTM can analyze time-series data in forward and backward passes on patient health metrics, such as blood glucose (BG) levels of a diabetic patient, to identify patterns and trends and make predictions about future health outcomes. A local dataset using a wearable sensor is collected of ten Type-2 diabetic patients, encompassing daily BG levels at different times, alongside additional parameters, such as blood pressure and body weight. The proposed framework leverages a dataset of 1830 data points to forecast glucose levels for the following day. It also harnesses DRL to improve and optimize the model’s future predictive performance. The obtained results are evaluated with other ML algorithms to validate the effectiveness of the proposed framework, which shows an improvement from 93.1% to 98.6% accuracy. The patient’s diabetic condition is categorized using the surveillance error grid (SEG) to increase the clinical impact of glucose prediction and make informed decisions. The results show that Bi-LSTM-DRL is an effective approach to predicting glucose levels in real-time and can adapt to health-related changes and optimize its predictions accordingly.