{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:35:09Z","timestamp":1775756109472,"version":"3.50.1"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"IIT Mandi Innovation Hub (iHub) and Human-Computer Interaction (HCI) Foundation","award":["IITM\/iHub and HCIF\u2014IIT Mandi\/VD\/412"],"award-info":[{"award-number":["IITM\/iHub and HCIF\u2014IIT Mandi\/VD\/412"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>Diabetes mellitus is a chronic metabolic disorder that necessitates frequent blood glucose monitoring, usually through painful and inconvenient methods. Volatile organic compounds (VOCs) in breath have been used as biomarkers for diabetes detection in non-invasive, Internet of Things (IoT)-based devices. Nevertheless, the cost, compactness, and mobility challenges of existing devices limit their general adoption. We present DiaBreath, a novel, affordable, non-invasive multi-sensor device for the early prediction of diabetes, solving these challenges. DiaBreath consists of (a) a breath analyzer containing MOS-based sensors, optimally selected via an ablation study to capture VOC responses (b) a feature engineering pipeline to to extract feature set, (c) a machine-learning model for reliable diabetes prediction, and (d) a simple user interface that generates prediagnostic diabetes reports. DiaBreath exhibits superior predictive power, with an accuracy of 97.6%, to enable efficient and scalable early diagnosis in public health centers, especially in resource-constrained settings. DiaBreath\u2019s low cost and compact size make it highly adaptable for implementation in rural and underserved regions, where access to timely diabetes screening is limited. This technology improves non-invasive diabetes monitoring, making early diagnosis more cost-effective and accessible globally.<\/jats:p>","DOI":"10.1145\/3768157","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:16:44Z","timestamp":1758028604000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["DiaBreath: A Low-Cost, Non-Invasive Diabetes Monitor via Breath"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2193-399X","authenticated-orcid":false,"given":"Ritik","family":"Sharma","sequence":"first","affiliation":[{"name":"IKSMHA Centre, IIT Mandi, Mandi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2151-8314","authenticated-orcid":false,"given":"Varun","family":"Dutt","sequence":"additional","affiliation":[{"name":"School of Computing and Electrical Engineering, IIT Mandi, Mandi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2849-4375","authenticated-orcid":false,"given":"Arnav","family":"Bhavsar","sequence":"additional","affiliation":[{"name":"School of Computing and Electrical Engineering, IIT Mandi, Mandi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7112-0630","authenticated-orcid":false,"given":"Ritu","family":"Kapur","sequence":"additional","affiliation":[{"name":"IIT Mandi, Mandi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4964-7094","authenticated-orcid":false,"given":"Bhupender","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Medicine, AIIMS Bilaspur, Bilaspur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8518-0765","authenticated-orcid":false,"given":"Vikrant","family":"Kanwar","sequence":"additional","affiliation":[{"name":"AIIMS Bilaspur, Bilaspur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat et al. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from https:\/\/www.tensorflow.org\/Software available from tensorflow.org."},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235663"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_3_5_2","doi-asserted-by":"crossref","DOI":"10.1201\/9781315139470","volume-title":"Classification and Regression Trees","author":"Leo Breiman.","year":"2017","unstructured":"Leo Breiman. 2017. Classification and Regression Trees. Routledge."},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"e_1_3_3_7_2","first-page":"1","article-title":"Type 2 diabetes mellitus","volume":"1","author":"DeFronzo Ralph A.","year":"2015","unstructured":"Ralph A. DeFronzo, Ele Ferrannini, Leif Groop, Robert R. Henry, William H. Herman, Jens Juul Holst, Frank B. Hu, C. Ronald Kahn, Itamar Raz, Gerald I. Shulman, et al. 2015. Type 2 diabetes mellitus. Nature reviews Disease Primers 1, 1 (2015), 1\u201322.","journal-title":"Nature reviews Disease Primers"},{"issue":"2","key":"e_1_3_3_8_2","article-title":"Systematic review of use of blood glucose test strips for the management of diabetes mellitus","volume":"1","author":"Canadian Agency for Drugs, Technologies in Health (CADTH)","year":"2010","unstructured":"Canadian Agency for Drugs, Technologies in Health (CADTH). 2010. Systematic review of use of blood glucose test strips for the management of diabetes mellitus. CADTH Technology Overviews 1, 2 (2010), e0101.","journal-title":"CADTH Technology Overviews"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2008.0005"},{"key":"e_1_3_3_12_2","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning. Vol. 37 PMLR 448\u2013456. Retrieved from https:\/\/proceedings.mlr.press\/v37\/ioffe15.html"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3392015"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.8b05928"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17040402"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3433987"},{"issue":"17","key":"e_1_3_3_17_2","first-page":"1","article-title":"Imbalanced-learn: A Python Toolbox to tackle the curse of imbalanced datasets in machine learning","volume":"18","author":"Lema\u00eetre Guillaume","year":"2017","unstructured":"Guillaume Lema\u00eetre, Fernando Nogueira, and Christos K. Aridas. 2017. Imbalanced-learn: A Python Toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research 18, 17 (2017), 1\u20135. DOI: http:\/\/jmlr.org\/papers\/v18\/16-365.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0204425"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3373646"},{"key":"e_1_3_3_20_2","volume-title":"Neural Networks and Deep Learning","author":"Nielsen Michael A.","year":"2015","unstructured":"Michael A. Nielsen. 2015. Neural Networks and Deep Learning. Vol. 25. Determination Press San Francisco, CA."},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"issue":"3","key":"e_1_3_3_22_2","doi-asserted-by":"crossref","first-page":"453","DOI":"10.3390\/diagnostics11030453","article-title":"Changes in salivary amylase and glucose in diabetes: A scoping review","volume":"11","author":"P\u00e9rez-Ros Pilar","year":"2021","unstructured":"Pilar P\u00e9rez-Ros, Emmanuel Navarro-Flores, Ivan Juli\u00e1n-Rochina, Francisco Miguel Mart\u00ednez-Arnau, and Omar Cauli. 2021. Changes in salivary amylase and glucose in diabetes: A scoping review. Diagnostics 11, 3 (2021), 453.","journal-title":"Diagnostics"},{"key":"e_1_3_3_23_2","volume-title":"Global Report on Diabetes","author":"Roglic Gojka","year":"2016","unstructured":"Gojka Roglic. 2016. Global Report on Diabetes. World Health Organization."},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2021.109119"},{"issue":"1","key":"e_1_3_3_26_2","doi-asserted-by":"crossref","first-page":"15707","DOI":"10.1038\/s41598-019-52165-x","article-title":"Exhaled volatile substances in children suffering from type 1 diabetes mellitus: Results from a cross-sectional study","volume":"9","author":"Trefz Phillip","year":"2019","unstructured":"Phillip Trefz, Juliane Obermeier, Ruth Lehbrink, Jochen K Schubert, Wolfram Miekisch, and Dagmar-Christiane Fischer. 2019. Exhaled volatile substances in children suffering from type 1 diabetes mellitus: Results from a cross-sectional study. Scientific Reports 9, 1 (2019), 15707.","journal-title":"Scientific Reports"},{"key":"e_1_3_3_27_2","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Vapnik Vladimir","year":"1995","unstructured":"Vladimir Vapnik. 1995. Support-vector networks. Machine Learning 20 (1995), 273\u2013297.","journal-title":"Machine Learning"},{"issue":"8747","key":"e_1_3_3_28_2","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/0140-6736(91)91569-G","article-title":"High breath pentane concentrations during acute myocardial infarction","volume":"337","author":"Weitz Z. W.","year":"1991","unstructured":"Z. W. Weitz, A. J. Birnbaum, J. L. Skosey, P. A. Sobotka, and E. J. Zarling. 1991. High breath pentane concentrations during acute myocardial infarction. The Lancet 337, 8747 (1991), 933\u2013935.","journal-title":"The Lancet"},{"issue":"48","key":"e_1_3_3_29_2","doi-asserted-by":"crossref","first-page":"25430","DOI":"10.1039\/C4RA01422G","article-title":"Discovery of potential biomarkers in exhaled breath for diagnosis of type 2 diabetes mellitus based on GC-MS with metabolomics","volume":"4","author":"Yan Yanyue","year":"2014","unstructured":"Yanyue Yan, Qihui Wang, Wenwen Li, Zhongjun Zhao, Xin Yuan, Yanping Huang, and Yixiang Duan. 2014. Discovery of potential biomarkers in exhaled breath for diagnosis of type 2 diabetes mellitus based on GC-MS with metabolomics. RSC Advances 4, 48 (2014), 25430\u201325439.","journal-title":"RSC Advances"}],"container-title":["ACM Transactions on Computing for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3768157","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:54:37Z","timestamp":1768395277000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3768157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,14]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,31]]}},"alternative-id":["10.1145\/3768157"],"URL":"https:\/\/doi.org\/10.1145\/3768157","relation":{},"ISSN":["2691-1957","2637-8051"],"issn-type":[{"value":"2691-1957","type":"print"},{"value":"2637-8051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,14]]},"assertion":[{"value":"2024-11-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}