Leveraging deep learning models for continuous glucose monitoring and prediction in diabetes management: towards enhanced blood sugar control

A. R. Mohamed Yousuff (), M. Zainulabedin Hasan, R. Anand and M. Rajasekhara Babu
Additional contact information
A. R. Mohamed Yousuff: Madanapalle Institute of Technology and Science
M. Zainulabedin Hasan: Jazan University
R. Anand: Abercrombie & Fitch
M. Rajasekhara Babu: Vellore Institute of Technology

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 6, No 5, 2077-2084

Abstract: Abstract Diabetes mellitus is a chronic metabolic disorder that affects millions of individuals worldwide, presenting significant challenges in disease management and long-term complications. Continuous Glucose Monitoring (CGM) has emerged as a valuable tool for monitoring blood glucose levels in diabetic patients, offering real-time data for enhanced disease control. However, the ability to predict glucose fluctuations in advance can greatly improve management strategies and minimize the risk of hyperglycemic or hypoglycemic episodes. This research paper proposes a novel approach to diabetes management by leveraging deep learning algorithms for CGM data analysis and prediction. The model utilizes a vast dataset of CGM readings, patient characteristics, and lifestyle factors, enabling it to recognize complex patterns and trends in glucose fluctuations. Through recurrent neural networks and other deep learning architectures, the model can learn from temporal dependencies in the data, making accurate predictions about future glucose levels. The predictive capabilities of the deep learning model offer personalized insights, alerting patients and healthcare providers to potential glucose excursions before they occur. Such early warnings enable timely adjustments to medication, diet, and lifestyle, promoting improved blood sugar control and reducing the risk of diabetes-related complications. While the potential of deep learning in diabetes management is promising, this research paper highlights the importance of rigorous validation and integration into clinical practice. Challenges such as data quality, model interpretability, and patient engagement must be addressed for successful implementation. In conclusion, this research presents a groundbreaking application of deep learning in diabetes management, demonstrating the potential to transform the way blood glucose levels are monitored and predicted. By harnessing the power of advanced data analytics, this model can pave the way towards personalized and proactive diabetes care, leading to better patient outcomes and ultimately enhancing the quality of life for individuals living with diabetes.

Keywords: Deep learning; Continuous glucose monitoring; Electronic health records; Glucose prediction; Risk assessment (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-023-02200-y

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