Deep Learning-Driven Smart Wearable for Early Prediction and Prevention of Diabetic Complications

Abstract

Diabetes mellitus is a chronic condition that significantly increases the risk of severe complications such as neuropathy, retinopathy, and cardiovascular diseases. Effective management requires continuous monitoring and early intervention to prevent irreversible damage. Traditional glucose monitoring methods are often invasive and do not provide real-time predictive insights into potential complications. Existing approaches primarily rely on periodic clinical assessments or non-personalized predictive models, limiting their effectiveness in real-world applications. Moreover, current smart wearables lack robust predictive analytics tailored specifically for diabetes management. This study introduces a deep learning-based smart wearable system designed to predict and prevent diabetic complications using multimodal sensor data, including heart rate variability, skin temperature, galvanic skin response, and glucose levels. A hybrid deep neural network (DNN) framework integrates convolutional and recurrent layers to process time-series data efficiently. The system employs an adaptive learning mechanism to personalize risk assessment based on individual health patterns. The model was trained and validated on a dataset collected from diabetic patients, achieving an accuracy of 98.4% in predicting early-stage complications. Additionally, the wearable provides real-time alerts and personalized lifestyle recommendations to mitigate risks. The proposed system shows superior performance compared to existing models, enhancing proactive healthcare for diabetic individuals.