DEEP LEARNING AND WEARABLE IOT SENSOR INTEGRATION FOR REAL-TIME PREDICTION AND MANAGEMENT OF DIABETIC COMPLICATIONS

Abstract

Diabetes mellitus is a chronic metabolic disorder that significantly increases the risk of severe complications including diabetic neuropathy, retinopathy, nephropathy, and cardiovascular diseases. Early prediction and continuous monitoring of these complications remain major challenges in modern healthcare systems due to the dynamic nature of physiological changes and the limitations of traditional clinical assessment methods. This research proposes DeepDiabNet, an intelligent healthcare framework that integrates wearable Internet of Things (IoT) sensors with deep learning techniques for real-time prediction and management of diabetic complications. The proposed framework utilizes a large-scale healthcare dataset containing physiological, behavioral, and clinical attributes including glucose levels, HbA1c values, blood pressure, heart rate, oxygen saturation, physical activity, sleep duration, and diabetes duration. Data preprocessing techniques such as label encoding, feature scaling, and normalization are applied to improve data quality and model performance. A Deep Neural Network (DNN) architecture incorporating multiple dense layers, batch normalization, and dropout regularization is developed to perform multiclass diabetic neuropathy risk classification.

Experimental evaluation demonstrates effective learning behavior, stable convergence characteristics, and strong classification capability across multiple risk categories. Performance assessment is conducted using accuracy, precision, recall, F1-score, confusion matrix analysis, and Receiver Operating Characteristic (ROC) evaluation. The results indicate that integrating wearable healthcare monitoring with deep learning analytics enhances diabetic complication prediction and supports early healthcare intervention. Furthermore, the proposed framework contributes toward the development of intelligent, scalable, and patient-centric healthcare ecosystems capable of enabling continuous disease monitoring, personalized healthcare management, and data-driven clinical decision support. The framework also provides a foundation for future integration of explainable artificial intelligence and precision healthcare technologies. Supported by recent advances in wearable AI healthcare systems and diabetes monitoring research, the framework demonstrates strong potential for next-generation diabetic healthcare applications.