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Identifying the Association of Time-Averaged Serum Albumin Levels with Clinical Factors among Patients on Hemodialysis Using Whale Optimization Algorithm

Author

Listed:
  • Cheng-Hong Yang

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
    Ph. D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
    School of Dentistry, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

  • Yin-Syuan Chen

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Sin-Hua Moi

    (Center of Cancer Program Development, E-Da Cancer Hospital, I-Shou University, Kaohsiung 82445, Taiwan)

  • Jin-Bor Chen

    (Department of Internal Medicine, Division of Nephrology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833401, Taiwan)

  • Li-Yeh Chuang

    (Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84004, Taiwan)

Abstract

Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered as a surrogate indicator of nutritional status. The whale optimization algorithm-based feature selection (WOFS) model could address the complex association between the clinical factors, and could further combine with regression models for application. The present study aimed to demonstrate an optimal multifactor TSA-associated model, in order to interpret the complex association between TSA and clinical factors among HD patients. A total of 829 HD patients who met the inclusion criteria were selected for analysis. Monthly serum albumin data tracked from January 2009 to December 2013 were converted into TSA categories based on a critical value of 3.5 g/dL. Multivariate logistic regression was used to analyze the association between TSA categories and multiple clinical factors using three types of feature selection models, namely the fully adjusted, stepwise, and WOFS models. Five features, albumin, age, creatinine, potassium, and HD adequacy index (Kt/V level), were selected from fifteen clinical factors by the WOFS model, which is the minimum number of selected features required in multivariate regression models for optimal multifactor model construction. The WOFS model yielded the lowest Akaike information criterion (AIC) value, which indicated that the WOFS model could achieve superior performance in the multifactor analysis of TSA for HD patients. In conclusion, the application of the optimal multifactor TSA-associated model could facilitate nutritional status monitoring in HD patients.

Suggested Citation

  • Cheng-Hong Yang & Yin-Syuan Chen & Sin-Hua Moi & Jin-Bor Chen & Li-Yeh Chuang, 2022. "Identifying the Association of Time-Averaged Serum Albumin Levels with Clinical Factors among Patients on Hemodialysis Using Whale Optimization Algorithm," Mathematics, MDPI, vol. 10(7), pages 1-12, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1030-:d:777786
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    References listed on IDEAS

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    1. Yuichi Nakazato & Riichi Kurane & Satoru Hirose & Akihisa Watanabe & Hiromi Shimoyama, 2017. "Aging and death-associated changes in serum albumin variability over the course of chronic hemodialysis treatment," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-17, September.
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    Cited by:

    1. Alma Y. Alanis, 2022. "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications," Mathematics, MDPI, vol. 10(13), pages 1-2, July.

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