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Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm
Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9859340/ https://www.ncbi.nlm.nih.gov/pubmed/36673588 http://dx.doi.org/10.3390/healthcare11020220 |
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author | Butkevičiūtė, Eglė Bikulčienė, Liepa Žvironienė, Aušra |
author_facet | Butkevičiūtė, Eglė Bikulčienė, Liepa Žvironienė, Aušra |
author_sort | Butkevičiūtė, Eglė |
collection | PubMed |
description | Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee’s physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems. |
format | Online Article Text |
id | pubmed-9859340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98593402023-01-21 Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm Butkevičiūtė, Eglė Bikulčienė, Liepa Žvironienė, Aušra Healthcare (Basel) Article Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee’s physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems. MDPI 2023-01-11 /pmc/articles/PMC9859340/ /pubmed/36673588 http://dx.doi.org/10.3390/healthcare11020220 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Butkevičiūtė, Eglė Bikulčienė, Liepa Žvironienė, Aušra Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm |
title | Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm |
title_full | Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm |
title_fullStr | Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm |
title_full_unstemmed | Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm |
title_short | Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm |
title_sort | physiological state evaluation in working environment using expert system and random forest machine learning algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9859340/ https://www.ncbi.nlm.nih.gov/pubmed/36673588 http://dx.doi.org/10.3390/healthcare11020220 |
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