A Deep Learning-based Predictive Analytics Model for Remote Patient Monitoring and Early Intervention in Diabetes Care

Abstract

This paper presents a deep learning-based predictive analytics model for remote diabetes monitoring and early intervention. The proposed method combines photoplethysmography (PPG) signals with population and clinical data by combining LSTM-CNN architecture, achieving the best glucose monitoring results in real time. Manage the inability to care. The system architecture includes a custom-designed wearable device for data acquisition, cloud-based infrastructure, and real-time intervention mechanisms. Validation tests, including 139 subjects (69 diabetics and 70 non-diabetic), showed a 91.2% prediction accuracy over the continuous product to check glucose. The application has achieved 99.7% uptime with a response time of 2.3 seconds, ensuring adequate monitoring time and quick response. The early warning system demonstrated 97.8% accuracy in detecting potential complications through innovative feature extraction methodologies and adaptive learning algorithms. Performance evaluation through Clarke Error Grid analysis indicated clinically acceptable predictions, with all readings falling within zones A and B. The system's cost-effectiveness and reduced invasiveness promote widespread adoption potential, particularly in resource-limited settings. Integrating existing medical systems enables data collection and analysis, facilitating personalized treatment strategies and improving patient outcomes. The research has advanced the level of diabetes management through new contributions to theoretical frameworks and practical applications in remote patient care.