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Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree
During the process of designing and implementing a working environment, there is a need to guarantee adequate conditions for future workers’ health and well-being. This article addresses the classification of employees characterized by several basic input variables (gender, age, class of work). The...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950521/ https://www.ncbi.nlm.nih.gov/pubmed/31842434 http://dx.doi.org/10.3390/ijerph16245080 |
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author | Andrejiová, Miriam Piňosová, Miriama Králiková, Ružena Dolník, Bystrík Liptai, Pavol Dolníková, Erika |
author_facet | Andrejiová, Miriam Piňosová, Miriama Králiková, Ružena Dolník, Bystrík Liptai, Pavol Dolníková, Erika |
author_sort | Andrejiová, Miriam |
collection | PubMed |
description | During the process of designing and implementing a working environment, there is a need to guarantee adequate conditions for future workers’ health and well-being. This article addresses the classification of employees characterized by several basic input variables (gender, age, class of work). The investigated variable was the health of employees. This article aims to create a prediction classification model using the classification tree, which can be used to classify new cases into appropriate classes as accurately as possible. Objective measurements of microclimatic parameters were performed by the Testo 435 instrument. The subjective evaluation was performed by a questionnaire survey formed from the training group of 80 respondents and independently verified by the test group of 80 more respondents. The result confusion matrix shows that the number of correctly classified respondents was 69 from a total of 80 respondents. The overall accuracy was [Formula: see text] , which means that the likelihood that respondents are properly classified in the correct health class is 86.3%. Based on the model obtained using the classification tree, we can classify respondents into the relevant class for their state of health. The respondent is classified into the class of work for which particular health and working conditions are most likely. |
format | Online Article Text |
id | pubmed-6950521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69505212020-01-16 Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree Andrejiová, Miriam Piňosová, Miriama Králiková, Ružena Dolník, Bystrík Liptai, Pavol Dolníková, Erika Int J Environ Res Public Health Article During the process of designing and implementing a working environment, there is a need to guarantee adequate conditions for future workers’ health and well-being. This article addresses the classification of employees characterized by several basic input variables (gender, age, class of work). The investigated variable was the health of employees. This article aims to create a prediction classification model using the classification tree, which can be used to classify new cases into appropriate classes as accurately as possible. Objective measurements of microclimatic parameters were performed by the Testo 435 instrument. The subjective evaluation was performed by a questionnaire survey formed from the training group of 80 respondents and independently verified by the test group of 80 more respondents. The result confusion matrix shows that the number of correctly classified respondents was 69 from a total of 80 respondents. The overall accuracy was [Formula: see text] , which means that the likelihood that respondents are properly classified in the correct health class is 86.3%. Based on the model obtained using the classification tree, we can classify respondents into the relevant class for their state of health. The respondent is classified into the class of work for which particular health and working conditions are most likely. MDPI 2019-12-12 2019-12 /pmc/articles/PMC6950521/ /pubmed/31842434 http://dx.doi.org/10.3390/ijerph16245080 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Andrejiová, Miriam Piňosová, Miriama Králiková, Ružena Dolník, Bystrík Liptai, Pavol Dolníková, Erika Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree |
title | Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree |
title_full | Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree |
title_fullStr | Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree |
title_full_unstemmed | Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree |
title_short | Analysis of the Impact of Selected Physical Environmental Factors on the Health of Employees: Creating a Classification Model Using a Decision Tree |
title_sort | analysis of the impact of selected physical environmental factors on the health of employees: creating a classification model using a decision tree |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950521/ https://www.ncbi.nlm.nih.gov/pubmed/31842434 http://dx.doi.org/10.3390/ijerph16245080 |
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