IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i8p1232-d1378948.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/8/1232/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/8/1232/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chun-Chang Lee & Chih-Min Liang & Hsing-Jung Chou, 2013. "Identifying Taiwan real estate cycle turning points- An application of the multivariate Markov-switching autoregressive Model," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 3(2), pages 1-1.
    2. Edmundas Kazimieras Zavadskas & Fausto Cavallaro & Valentinas Podvezko & Ieva Ubarte & Arturas Kaklauskas, 2017. "MCDM Assessment of a Healthy and Safe Built Environment According to Sustainable Development Principles: A Practical Neighborhood Approach in Vilnius," Sustainability, MDPI, vol. 9(5), pages 1-30, April.
    3. Robert H. McGuckin & Ataman Ozyildirim, 2004. "Real-Time Tests of the Leading Economic Index: Do Changes in the Index Composition Matter?," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(2), pages 171-191.
    4. Corradi, Valentina & Fernandez, Andres & Swanson, Norman R., 2009. "Information in the Revision Process of Real-Time Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 455-467.
    5. Helmut Herwartz & Christoph Strumann, 2024. "Too many cooks could spoil the broth: choice overload and the provision of ambulatory health care," International Journal of Health Economics and Management, Springer, vol. 24(3), pages 357-373, September.
    6. Mili, Mehdi & Sahut, Jean-Michel & Teulon, Frédéric, 2012. "Non linear and asymmetric linkages between real growth in the Euro area and global financial market conditions: New evidence," Economic Modelling, Elsevier, vol. 29(3), pages 734-741.
    7. Ni, Lei & Chen, Yu-wang & de Brujin, Oscar, 2021. "Towards understanding socially influenced vaccination decision making: An integrated model of multiple criteria belief modelling and social network analysis," European Journal of Operational Research, Elsevier, vol. 293(1), pages 276-289.
    8. Dagum, Estela Bee, 2010. "Business Cycles and Current Economic Analysis/Los ciclos económicos y el análisis económico actual," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 28, pages 577-594, Diciembre.
    9. Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.
    10. Jackson, Emerson Abraham & Tamuke, Edmund, 2021. "The Science and Art of Communicating Fan Chart Uncertainty: The case of Inflation Outcome in Sierra Leone," MPRA Paper 105892, University Library of Munich, Germany, revised 05 Jan 2021.
    11. Audrius Čereška & Edmundas Kazimieras Zavadskas & Fausto Cavallaro & Valentinas Podvezko & Ina Tetsman & Irina Grinbergienė, 2016. "Sustainable Assessment of Aerosol Pollution Decrease Applying Multiple Attribute Decision-Making Methods," Sustainability, MDPI, vol. 8(7), pages 1-12, June.
    12. Pratibha Rani & Arunodaya Raj Mishra & Abbas Mardani & Fausto Cavallaro & Dalia Štreimikienė & Syed Abdul Rehman Khan, 2020. "Pythagorean Fuzzy SWARA–VIKOR Framework for Performance Evaluation of Solar Panel Selection," Sustainability, MDPI, vol. 12(10), pages 1-18, May.
    13. Margaret M. McConnell & Gabriel Perez-Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    14. Maximo Camacho & Gabriel Perez-Quiros, 2002. "This is what the leading indicators lead," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 61-80.
    15. Askoldas Podviezko & Lyudmila Parfenova & Andrey Pugachev, 2019. "Tax Competitiveness of the New EU Member States," JRFM, MDPI, vol. 12(1), pages 1-19, February.
    16. Marcio Pereira Basilio & Valdecy Pereira & Fatih Yigit, 2023. "New Hybrid EC-Promethee Method with Multiple Iterations of Random Weight Ranges: Applied to the Choice of Policing Strategies," Mathematics, MDPI, vol. 11(21), pages 1-34, October.
    17. Huibing Cheng & Shanshui Zheng & Jianghong Feng, 2022. "A Fuzzy Multi-Criteria Method for Sustainable Ferry Operator Selection: A Case Study," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    18. Rađenović Žarko & Veselinović Ivana, 2017. "Integrated AHP-TOPSIS Method for the Assessment of Health Management Information Systems Efficiency," Economic Themes, Sciendo, vol. 55(1), pages 121-142, March.
    19. Leiva-Leon, Danilo, 2013. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," MPRA Paper 54452, University Library of Munich, Germany.
    20. Ulutaş Alptekin & Karaköy Çağatay, 2019. "An analysis of the logistics performance index of EU countries with an integrated MCDM model," Economics and Business Review, Sciendo, vol. 5(4), pages 49-69, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1232-:d:1378948. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.