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Algorithm for Determination of Indicators Predicting Health Status for Health Monitoring Process Optimization

Author

Listed:
  • Aleksandras Krylovas

    (Department of Mathematical Modelling, Vilnius Gediminas Technical University, Sauletekio al. 11, 10221 Vilnius, Lithuania)

  • Natalja Kosareva

    (Department of Mathematical Modelling, Vilnius Gediminas Technical University, Sauletekio al. 11, 10221 Vilnius, Lithuania)

  • Stanislav Dadelo

    (Department of Entertainment Industries, Vilnius Gediminas Technical University, Sauletekio al. 11, 10221 Vilnius, Lithuania)

Abstract

This article proposes an algorithm that allows the selection of prognostic variables from a set of 21 variables describing the health statuses of male and female students. The set of variables could be divided into two groups—body condition indicators and body activity indicators. For this purpose, we propose applying the multiple criteria decision methods WEBIRA, entropy-ARAS, and SAW in modelling the general health index, a latent variable describing health status, which is used to rank the alternatives. In the next stage, applying multiple regression analysis, the most informative indicators influencing health status are selected by reducing the indicator’s number to 9–11, and predictor indicators by reducing their number to 5. A methodology for grouping students into three groups is proposed, using selected influencing indicators and predictor indicators in regression equations with the dependent variable of group number. Our study revealed that two body condition indicators and three body activity indicators have the greatest influence on men’s general health index. It was established that two body condition indicators have the greatest influence on women’s general health index. The determination of the most informative indicators is important for predicting health status and optimizing the health monitoring process.

Suggested Citation

  • Aleksandras Krylovas & Natalja Kosareva & Stanislav Dadelo, 2024. "Algorithm for Determination of Indicators Predicting Health Status for Health Monitoring Process Optimization," Mathematics, MDPI, vol. 12(8), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1232-:d:1378948
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    References listed on IDEAS

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    1. Edmundas Kazimieras Zavadskas & Valentinas Podvezko, 2016. "Integrated Determination of Objective Criteria Weights in MCDM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 267-283, March.
    2. Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
    3. Weaver III, J.B. & Mays, D. & Weaver, S.S. & Hopkins, G.L. & Eroglu, D. & Bernhardt, J.M., 2010. "Health information-seeking behaviors, health indicators, and health risks," American Journal of Public Health, American Public Health Association, vol. 100(8), pages 1520-1525.
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