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Comparative Study of Machine Learning Methods on Spectroscopy Images for Blood Glucose Estimation

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Pervasive Computing Technologies for Healthcare (PH 2022)

Abstract

Diabetes and metabolic diseases are considered a silent epidemic in the United States. Monitoring blood glucose, the lead indicator of these diseases, involves either a cumbersome process of extracting blood several times per day or implanting needles under the skin. However, new technologies have emerged for non-invasive blood glucose monitoring, including light absorption and spectroscopy methods. In this paper, we performed a comparative study of diverse Machine Learning (ML) methods on spectroscopy images to estimate blood glucose concentration. We used a database of fingertip images from 45 human subjects and trained several ML methods based on image tensors, color intensity, and statistical image information. We determined that for spectroscopy images, AdaBoost trained with KNeigbors is the best model to estimate blood glucose with a percentage of 90.78% of results in zone “A” (accurate) and 9.22% in zone “B” (clinically acceptable) according to the Clarke Error Grid metric.

Supported by College of Computing and Software Engineering and Office of Research at Kennesaw State University.

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Authors and Affiliations

  1. Department of Information Technology, Kennesaw State University, Kennesaw, GA, 30060, USA

    Tahsin Kazi, Kiran Ponakaladinne, Maria Valero, Liang Zhao & Hossain Shahriar

  2. Department of Exercise Science and Sport Management, Kennesaw State University, Kennesaw, GA, 30060, USA

    Katherine H. Ingram

Authors
  1. Tahsin Kazi
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  2. Kiran Ponakaladinne
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  3. Maria Valero
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  4. Liang Zhao
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  5. Hossain Shahriar
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  6. Katherine H. Ingram
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Corresponding author

Correspondence to Maria Valero .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  2. Centre for Research and Technology Hellas, Thessaloniki, Greece

    Andreas Triantafyllidis

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Kazi, T., Ponakaladinne, K., Valero, M., Zhao, L., Shahriar, H., Ingram, K.H. (2023). Comparative Study of Machine Learning Methods on Spectroscopy Images for Blood Glucose Estimation. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_5

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  • DOI: https://doi.org/10.1007/978-3-031-34586-9_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-34586-9

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