SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914184753577984 |
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| author | Henriques, Ian Elhassar, Lynda Relekar, Sarvesh Walrave, Denis Hassantabar, Shayan Ghanakota, Vishu Laoui, Adel Aich, Mahmoud Tir, Rafia Zerguine, Mohamed Louafi, Samir Kimouche, Moncef Cosson, Emmanuel Jha, Niraj K |
| author_facet | Henriques, Ian Elhassar, Lynda Relekar, Sarvesh Walrave, Denis Hassantabar, Shayan Ghanakota, Vishu Laoui, Adel Aich, Mahmoud Tir, Rafia Zerguine, Mohamed Louafi, Samir Kimouche, Moncef Cosson, Emmanuel Jha, Niraj K |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening Henriques, Ian Elhassar, Lynda Relekar, Sarvesh Walrave, Denis Hassantabar, Shayan Ghanakota, Vishu Laoui, Adel Aich, Mahmoud Tir, Rafia Zerguine, Mohamed Louafi, Samir Kimouche, Moncef Cosson, Emmanuel Jha, Niraj K Machine Learning Computers and Society 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. |
| title | SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2512.03471 |