Artificial Intelligence and Its Implementation in Diabetes Management and Education

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 12

Year of Publication : 2025

Authors : Analene Montesines Nagayo, Arkiath Veettil Raveendran

10.14445/23488549/IJECE-V12I12P110

How to Cite?

Analene Montesines Nagayo, Arkiath Veettil Raveendran, "Artificial Intelligence and Its Implementation in Diabetes Management and Education," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 12, pp. 114-133, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I12P110

Abstract:

This paper outlines the Artificial Intelligence (AI)-based solutions in managing Diabetes Mellitus (DM) and raising awareness about the disease. It also discusses the architecture of a designed e-health platform that integrates Internet of Things (IoT), Mobile Computing, and Machine Learning (ML) methods for managing diabetes and forecasting the risk of acquiring the disease. The patient-centered platform involves the development and integration of the following subsystems: (a) an IoT-enabled physiological signs and blood glucose monitoring system that allows real-time acquisition and analysis of patients’ diabetes related symptoms; (b) interactive smartphone and web-based applications that allow patients to track their health status and risk factors for diabetes, respond to question lifestyle practices and family history of diabetes, record blood glucose measurements, facilitate doctor-patient online communication, and enable doctors to enter medical results, diagnosis and treatment plans; and (c) an ensemble ML-based model for the majority voting prediction of clinical health risk due to diabetes and its complications. The preliminary results demonstrated that the Random Forest (RF) algorithm performed well relative to the Logistic Regression (LR) and Naïve Bayes (NB) approaches, with an accuracy of 97.8%. The developed ensemble ML-based model obtained a 97.8% overall accuracy, 98% precision, 97.8% recall, and 97.7% F1-score using majority voting with the RF technique as the tiebreaker. Furthermore, validation against actual clinical data showed that the predicted DM-related health risk levels were consistent with the assessments from medical experts and established clinical guidelines.

Keywords:

Diabetes management, Diabetes education, Artificial Intelligence, Machine Learning, IoT-based Monitoring.

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