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Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance

Author

Listed:
  • Gustavo A. Alonso-Silverio

    (Facultad de Ingeniería, Universidad Autónoma de Guerrero, Chilpancingo 39087, Mexico)

  • Víctor Francisco-García

    (Facultad de Ingeniería, Universidad Autónoma de Guerrero, Chilpancingo 39087, Mexico)

  • Iris P. Guzmán-Guzmán

    (Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Chilpancingo 39087, Mexico)

  • Elías Ventura-Molina

    (Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Mexico City 07700, Mexico)

  • Antonio Alarcón-Paredes

    (Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, Mexico City 07738, Mexico)

Abstract

The present study comprises a comparison of the Mel Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as feature extraction methods using ten different regression algorithms (AdaBoost, Bayesian Ridge, Decision Tree, Elastic Net, k-NN, Linear Regression, MLP, Random Forest, Ridge Regression and Support Vector Regression) to quantify the blood glucose concentration. A total of 122 participants—healthy and diagnosed with type 2 diabetes—were invited to be part of this study. The entire set of participants was divided into two partitions: a training subset of 72 participants, which was intended for model selection, and a validation subset comprising the remaining 50 participants, to test the selected model. A 3D-printed chamber for providing a light-controlled environment and a low-cost microcontroller unit were used to acquire optical measurements. The MFCC, PCA and ICA were calculated by an open-hardware computing platform. The glucose levels estimated by the system were compared to actual glucose concentrations measured by venipuncture in a laboratory test, using the mean absolute error, the mean absolute percentage error and the Clarke error grid for this purpose. The best results were obtained for MCCF with AdaBoost and Random Forest (MAE = 11.6 for both).

Suggested Citation

  • Gustavo A. Alonso-Silverio & Víctor Francisco-García & Iris P. Guzmán-Guzmán & Elías Ventura-Molina & Antonio Alarcón-Paredes, 2021. "Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance," Mathematics, MDPI, vol. 9(20), pages 1-13, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2529-:d:652113
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    References listed on IDEAS

    as
    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Tamar Lin & Avner Gal & Yulia Mayzel & Keren Horman & Karnit Bahartan, 2017. "Non-Invasive Glucose Monitoring: A Review of Challenges and Recent Advances," Current Trends in Biomedical Engineering & Biosciences, Juniper Publishers Inc., vol. 6(5), pages 83-90, July.
    3. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    4. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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