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

Aleksandras Krylovas, Natalja Kosareva and Stanislav Dadelo ()
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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

Mathematics, 2024, vol. 12, issue 8, 1-23

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.

Keywords: body condition; body activity; MCDM methods; entropy; regression analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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