A Deep Learning-based Predictive Analytics Model for Remote Patient Monitoring and Early Intervention in Diabetes Care
DOI:
https://doi.org/10.53469/wjimt.2024.07(06).17Keywords:
Diabetes monitoring, Deep learning, Photoplethysmography, Remote patient monitorin, Predictive analyticsAbstract
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.
References
Panda, R., Dash, S., Padhy, S., & Das, R. K. (2022). Diabetes mellitus prediction through interactive machine learning approaches. In Next Generation of Internet of Things: Proceedings of ICNGIoT 2022 (pp. 143-152). Singapore: Springer Nature Singapore.
Shuzan, M. N. I., Chowdhury, M. H., Chowdhury, M. E., Abualsaud, K., Yaacoub, E., Faisal, M. A. A., ... & Zorba, N. (2024). QU-GM: An IoT Based Glucose Monitoring System from Photoplethysmography, Blood Pressure and Demographic Data using Machine Learning. IEEE Access.
Siddiqui, M. M., Malick, R. A. S., & Ahmed, G. (2022, October). LSTM Based Deep Learning Model for Blood Sugar Prediction. In 2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) (pp. 1-4). IEEE.
Sharma, S. K., Zamani, A. T., Abdelsalam, A., Muduli, D., Alabrah, A. A., Parveen, N., & Alanazi, S. M. (2023). A diabetes monitoring system and health-medical service composition model in cloud environment. IEEE Access, 11, 32804-32819.
Usha, V., & Rajalakshmi, N. R. (2023, September). Insights into Diabetes Prediction: A Multi-Algorithm Machine Learning Analysis. In 2023 4th International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1207-1212). IEEE.
Che, C., Huang, Z., Li, C., Zheng, H., & Tian, X. (2024). Integrating generative ai into financial market prediction for improved decision making. arXiv preprint arXiv:2404.03523.
Che, C., Zheng, H., Huang, Z., Jiang, W., & Liu, B. (2024). Intelligent robotic control system based on computer vision technology. arXiv preprint arXiv:2404.01116.
Jiang, Y., Tian, Q., Li, J., Zhang, M., & Li, L. (2024). The Application Value of Ultrasound in the Diagnosis of Ovarian Torsion. International Journal of Biology and Life Sciences, 7(1), 59-62.
Li, L., Li, X., Chen, H., Zhang, M., & Sun, L. (2024). Application of AI-assisted Breast Ultrasound Technology in Breast Cancer Screening. International Journal of Biology and Life Sciences, 7(1), 1-4.
Lijie, L., Caiying, P., Liqian, S., Miaomiao, Z., & Yi, J. The application of ultrasound automatic volume imaging in detecting breast tumors.
Ke, X., Li, L., Wang, Z., & Cao, G. (2024). A Dynamic Credit Risk Assessment Model Based on Deep Reinforcement Learning. Academic Journal of Natural Science, 1(1), 20-31.
Zhou, S., Zheng, W., Xu, Y., & Liu, Y. (2024). Enhancing User Experience in VR Environments through AI-Driven Adaptive UI Design. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 59-82.
Wang, S., Zhang, H., Zhou, S., Sun, J., & Shen, Q. (2024). Chip Floorplanning Optimization Using Deep Reinforcement Learning. International Journal of Innovative Research in Computer Science & Technology, 12(5), 100-109.
Wei, M., Pu, Y., Lou, Q., Zhu, Y., & Wang, Z. (2024). Machine Learning-Based Intelligent Risk Management and Arbitrage System for Fixed Income Markets: Integrating High-Frequency Trading Data and Natural Language Processing. Journal of Industrial Engineering and Applied Science, 2(5), 56-67.
Xu, K., Zhou, H., Zheng, H., Zhu, M., & Xin, Q. (2024). Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning. arXiv preprint arXiv:2403.19345.
Xu, K., Zheng, H., Zhan, X., Zhou, S., & Niu, K. (2024). Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility.
Zheng, H., Xu, K., Zhou, H., Wang, Y., & Su, G. (2024). Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis. Academic Journal of Science and Technology, 10(1), 62-68.
Zheng, H.; Wu, J.; Song, R.; Guo, L.; Xu, Z. Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Applied and Computational Engineering 2024, 87, 26–32.
Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 295-326.
Li, P., Hua, Y., Cao, Q., & Zhang, M. (2020, December). Improving the Restore Performance via Physical-Locality Middleware for Backup Systems. In Proceedings of the 21st International Middleware Conference (pp. 341-355).
Zhou, S., Yuan, B., Xu, K., Zhang, M., & Zheng, W. (2024). THE IMPACT OF PRICING SCHEMES ON CLOUD COMPUTING AND DISTRIBUTED SYSTEMS. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 193-205.
Shang, F., Zhao, F., Zhang, M., Sun, J., & Shi, J. (2024). Personalized Recommendation Systems Powered By Large Language Models: Integrating Semantic Understanding and User Preferences. International Journal of Innovative Research in Engineering and Management, 11(4), 39-49.
Sun, J., Wen, X., Ping, G., & Zhang, M. (2024). Application of News Analysis Based on Large Language Models in Supply Chain Risk Prediction. Journal of Computer Technology and Applied Mathematics, 1(3), 55-65.
Zhao, F., Zhang, M., Zhou, S., & Lou, Q. (2024). Detection of Network Security Traffic Anomalies Based on Machine Learning KNN Method. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 1(1), 209-218.
Ju, Chengru, and Yida Zhu. "Reinforcement Learning Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision Making." (2024).
Yu, Keke, et al. "Loan Approval Prediction Improved by XGBoost Model Based on Four-Vector Optimization Algorithm." (2024).
Zhou, S., Sun, J., & Xu, K. (2024). AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture.
Sun, J., Zhou, S., Zhan, X., & Wu, J. (2024). Enhancing Supply Chain Efficiency with Time Series Analysis and Deep Learning Techniques.
Zheng, H., Xu, K., Zhang, M., Tan, H., & Li, H. (2024). Efficient resource allocation in cloud computing environments using AI-driven predictive analytics. Applied and Computational Engineering, 82, 6-12.
Zheng, W., Yang, M., Huang, D., & Jin, M. (2024). A Deep Learning Approach for Optimizing Monoclonal Antibody Production Process Parameters. International Journal of Innovative Research in Computer Science & Technology, 12(6), 18-29.
Ma, X., Wang, J., Ni, X., & Shi, J. (2024). Machine Learning Approaches for Enhancing Customer Retention and Sales Forecasting in the Biopharmaceutical Industry: A Case Study. International Journal of Engineering and Management Research, 14(5), 58-75.
Li, L., Zhang, Y., Wang, J., & Ke, X. (2024). Deep Learning-Based Network Traffic Anomaly Detection: A Study in IoT Environments.
Cao, G., Zhang, Y., Lou, Q., & Wang, G. (2024). Optimization of High-Frequency Trading Strategies Using Deep Reinforcement Learning. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 230-257.
Wang, G., Ni, X., Shen, Q., & Yang, M. (2024). Leveraging Large Language Models for Context-Aware Product Discovery in E-commerce Search Systems. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(4).
Li, H., Wang, G., Li, L., & Wang, J. (2024). Dynamic Resource Allocation and Energy Optimization in Cloud Data Centers Using Deep Reinforcement Learning. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 1(1), 230-258.
Li, H., Sun, J., & Ke, X. (2024). AI-Driven Optimization System for Large-Scale Kubernetes Clusters: Enhancing Cloud Infrastructure Availability, Security, and Disaster Recovery. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), 281-306.
Xia, S., Wei, M., Zhu, Y., & Pu, Y. (2024). AI-Driven Intelligent Financial Analysis: Enhancing Accuracy and Efficiency in Financial Decision-Making. Journal of Economic Theory and Business Management, 1(5), 1-11.
Zhang, H., Lu, T., Wang, J., & Li, L. (2024). Enhancing Facial Micro-Expression Recognition in Low-Light Conditions Using Attention-guided Deep Learning. Journal of Economic Theory and Business Management, 1(5), 12-22.
Wang, J., Lu, T., Li, L., & Huang, D. (2024). Enhancing Personalized Search with AI: A Hybrid Approach Integrating Deep Learning and Cloud Computing. International Journal of Innovative Research in Computer Science & Technology, 12(5), 127-138.
Ma, X., Zeyu, W., Ni, X., & Ping, G. (2024). Artificial intelligence-based inventory management for retail supply chain optimization: a case study of customer retention and revenue growth. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(4), 260-273.
Yang, M., Huang, D., Zhang, H., & Zheng, W. (2024). AI-enabled precision medicine: Optimizing treatment strategies through genomic data analysis. Journal of Computer Technology and Applied Mathematics, 1(3), 73-84.
Huang, D., Yang, M., Wen, X., Xia, S., & Yuan, B. (2024). AI-DRIVEN DRUG DISCOVERY: ACCELERATING THE DEVELOPMENT OF NOVEL THERAPEUTICS IN BIOPHARMACEUTICALS. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 206-224.