Integration of an IoT-enabled Biomedical Telemetry System and AI Health Risk Assessment Model based on Fuzzy Logic for Managing Diabetes and Cardiovascular Diseases

Abstract

Individuals with diabetes mellitus (DM) and cardiovascular disease (CVD) are on the rise worldwide, affecting both children and adults. Early-onset symptoms of these non-communicable diseases must be monitored and treated to prevent complications and improve people's quality of life. To address this health issue, the following modules were designed, integrated and implemented: (a) a biomedical telemetry system using IoT-enabled Particle photon microcontrollers interfaced with biosensor and switches that remotely monitor physiological symptoms and anthropometric indicators associated with DM and CVD; (b) an AI assessment model based on fuzzy logic which assist endocrinologists, cardiologist, and other medical professionals in evaluating health risks associated with these conditions, and (c) an interactive smartphone application that allows medical professionals to track patients' well-being and make timely interventions or changes to treatment programs, as well as enabling patient to perform tele- or video-consultation with the doctor. All medical data were securely stored and accessible via the ThingSpeak cloud and MariaDB database servers. Based on the experimental data, the designed system demonstrated 96.19% accuracy in analyzing medical data and evaluating the patients’ health risk levels associated with DM and CVD when compared to medical algorithms, published clinical practice guidelines, and advice given by medical experts.  Furthermore, the estimated anthropometric parameters and body composition metrics calculated using the developed mobile app were comparable to those acquired using various medical algorithms and programs available online. As a whole, the integrated modules contributed to the Society 5.0 initiative by delivering patient-centered healthcare using IoT, mobile computing, and AI technologies.