Abstract
Diabetes is a metabolic disorder often diagnosed late and needs
continuous blood glucose monitoring. 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 analyzing 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.Â
Given a person’s breath sample, demographics, and body vitals data as
input, GlucoBreath predicts (a) if 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. Further, the BGL prediction by GlucoBreath can help
alert individuals to control their sugar consumption in case of a
moderate BGL or visit a physician in case of a high BGL. Â