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      Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

      , , , ,
      Sensors
      MDPI AG

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          Abstract

          Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm’s achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

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          IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045

          To provide global, regional, and country-level estimates of diabetes prevalence and health expenditures for 2021 and projections for 2045.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                SENSC9
                Sensors
                Sensors
                MDPI AG
                1424-8220
                February 2024
                February 17 2024
                : 24
                : 4
                : 1294
                Article
                10.3390/s24041294
                38400451
                95f251ba-c9e7-411a-89bd-2a26865b0ebc
                © 2024

                https://creativecommons.org/licenses/by/4.0/

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