Abstract:Diabetes is a global health burden, and early detection is critical for timely intervention. This study explores a non-invasive, data-driven framework to identify individuals at risk of diabetes using Volatile Organic Compounds (VOCs) and lifestyle variables. We use causal inference techniques to estimate the impact of VOCs such as acetone, isopropanol, isoprene, and ethanol on blood glucose levels. Additionally, we designed a classifier to distinguish diabetics from non-diabetics using non-invasive markers. We created a risk-based ranking system for individuals in the "gray zone," and identified natural clusters in the population using Gaussian Mixture Model. Our results suggest that specific VOCs exhibit a strong causal influence on glucose levels and that machine learning models can reliably classify and stratify individuals at high risk. This integrated causal-explainable analysis can support the development of tool for non-invasive early screening of diabetes.
| Subjects: | Machine Learning (cs.LG); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2605.22075 [cs.LG] |
| (or arXiv:2605.22075v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22075 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | Proceedings of the IJCAI workshop on Advanced Neural Systems for Next-Generation Biomedical Intelligence, 2025 |
From: Varsha Sharma [view email]
[v1]
Thu, 21 May 2026 07:13:28 UTC (55 KB)
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