Article
Comparative Study of Machine Learning Methods on Spectroscopy Images for Blood Glucose Estimation
Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
(2023)
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.
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
Disciplines
Publication Date
July, 2023
DOI
10.1007/978-3-031-34586-9_5
Citation Information
Tahsin Kazi, Kiran Ponakaladinne, Maria Valero, Liang Zhao, et al.. "Comparative Study of Machine Learning Methods on Spectroscopy Images for Blood Glucose Estimation" Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Vol. 488 (2023) Available at: http://works.bepress.com/katherine-h-ingram/23/