GlucoBreath: An IoT, ML, and Breath-Based Non-Invasive Glucose Meter

Abstract

Diabetes is a metabolic disorder often diagnosed late and requires continuous monitoring of blood glucose. We introduce GlucoBreath, a user-centric, cost-effective, and portable pre-diagnostic solution to address this global challenge. GlucoBreath addresses the urgent need for an accessible and non-intrusive diabetes detection device, offering affordability, mobility, and comfortable non-invasive diabetes testing, especially among economically weaker sections of society. GlucoBreath comprises (i) a non-intrusive multi-sensor Internet of Things device comprising multiple sensors detecting volatile organic compounds in breath, (ii) BreathProfiles dataset encompasses information from 492 patients, which includes demographic details, physiological measurements, and sensor readings derived by analysing breath samples with our device, (iii) an innovative Machine Learning-based diabetes prediction system trained on the BreathProfiles dataset, and (iv) a user-friendly web interface for seamless device interaction and viewing diabetes reports. Given a person's breath sample, demographic data, and body vitals as input, GlucoBreath predicts (a) whether the person has diabetes. (b) If the person has diabetes, then the blood glucose level (BGL) of the person is moderate or high. GlucoBreath's groundbreaking approach supersedes current methods, achieving an impressive mean accuracy of 98.4% using a Logistic Regression-AdaBoost stack-metamodel, marking a substantial 43.3% improvement over an existing method. Due to its portability, non-intrusiveness, and rapid response, GlucoBreath is a valuable pre-diagnostic tool that can facilitate the early detection of diabetes in many individuals. Furthermore, GlucoBreath's prediction of BGL can help alert people to control their sugar consumption in the case of moderate BGL or see a doctor for high BGL.