{"ID":2833321,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03471","arxiv_id":"2512.03471","title":"SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening","abstract":"The global rise in type 2 diabetes underscores the need for scalable and cost-effective screening methods. Current diagnosis requires biochemical assays, which are invasive and costly. Advances in consumer wearables have enabled early explorations of machine learning-based disease detection, but prior studies were limited to controlled settings. We present SweetDeep, a compact neural network trained on physiological and demographic data from 285 (diabetic and non-diabetic) participants in the EU and MENA regions, collected using Samsung Galaxy Watch 7 devices in free-living conditions over six days. Each participant contributed multiple 2-minute sensor recordings per day, totaling approximately 20 recordings per individual. Despite comprising fewer than 3,000 parameters, SweetDeep achieves 82.5% patient-level accuracy (82.1% macro-F1, 79.7% sensitivity, 84.6% specificity) under three-fold cross-validation, with an expected calibration error of 5.5%. Allowing the model to abstain on less than 10% of low-confidence patient predictions yields an accuracy of 84.5% on the remaining patients. These findings demonstrate that combining engineered features with lightweight architectures can support accurate, rapid, and generalizable detection of type 2 diabetes in real-world wearable settings.","short_abstract":"The global rise in type 2 diabetes underscores the need for scalable and cost-effective screening methods. Current diagnosis requires biochemical assays, which are invasive and costly. Advances in consumer wearables have enabled early explorations of machine learning-based disease detection, but prior studies were limi...","url_abs":"https://arxiv.org/abs/2512.03471","url_pdf":"https://arxiv.org/pdf/2512.03471v2","authors":"[\"Ian Henriques\",\"Lynda Elhassar\",\"Sarvesh Relekar\",\"Denis Walrave\",\"Shayan Hassantabar\",\"Vishu Ghanakota\",\"Adel Laoui\",\"Mahmoud Aich\",\"Rafia Tir\",\"Mohamed Zerguine\",\"Samir Louafi\",\"Moncef Kimouche\",\"Emmanuel Cosson\",\"Niraj K Jha\"]","published":"2025-12-03T05:52:26Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CY\"]","methods":"[\"LoRA\"]","has_code":false}