Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms
In this research, a model is proposed for predicting the number of days absent from work due to sick or health-related leave among workers in the industry sector, according to ergonomic, social and work-related factors. It employs selected microdata from the Sixth European Working Conditions Survey...
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doaj-b459cdb600284163972a4ae99e509a6c2020-11-25T02:33:18ZengMDPI AGEnergies1996-10732020-05-01132475247510.3390/en13102475Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic AlgorithmsAroa González Fuentes0Nélida M. Busto Serrano1Fernando Sánchez Lasheras2Gregorio Fidalgo Valverde3Ana Suárez Sánchez4School of Mining, Energy and Materials Engineering of Oviedo, University of Oviedo, 33007 Oviedo, SpainLabor and Social Security Inspectorate. Ministry of Labor and Social Economy, 33007 Oviedo, SpainMathematics Department, Faculty of Sciences, University of Oviedo, 33007 Oviedo, SpainDepartment of Business Administration, School of Mining, Energy and Materials Engineering of Oviedo, University of Oviedo, 33007 Oviedo, SpainDepartment of Business Administration, School of Mining, Energy and Materials Engineering of Oviedo, University of Oviedo, 33007 Oviedo, SpainIn this research, a model is proposed for predicting the number of days absent from work due to sick or health-related leave among workers in the industry sector, according to ergonomic, social and work-related factors. It employs selected microdata from the Sixth European Working Conditions Survey (EWCS) and combines a genetic algorithm with Multivariate Adaptive Regression Splines (MARS). The most relevant explanatory variables identified by the model can be included in the following categories: ergonomics, psychosocial factors, working conditions and personal data and physiological characteristics. These categories are interrelated, and it is difficult to establish boundaries between them. Any managing program has to act on factors that affect the employees' general health status, process design, workplace environment, ergonomics and psychosocial working context, among others, to achieve success. This has an extensive field of application in the energy sector.https://www.mdpi.com/1996-1073/13/10/2475sick leaveabsenteeismenergy sectorgenetic algorithms (GA)multivariate adaptive regression splines (MARS) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aroa González Fuentes Nélida M. Busto Serrano Fernando Sánchez Lasheras Gregorio Fidalgo Valverde Ana Suárez Sánchez |
spellingShingle |
Aroa González Fuentes Nélida M. Busto Serrano Fernando Sánchez Lasheras Gregorio Fidalgo Valverde Ana Suárez Sánchez Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms Energies sick leave absenteeism energy sector genetic algorithms (GA) multivariate adaptive regression splines (MARS) |
author_facet |
Aroa González Fuentes Nélida M. Busto Serrano Fernando Sánchez Lasheras Gregorio Fidalgo Valverde Ana Suárez Sánchez |
author_sort |
Aroa González Fuentes |
title |
Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms |
title_short |
Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms |
title_full |
Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms |
title_fullStr |
Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms |
title_full_unstemmed |
Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms |
title_sort |
prediction of health-related leave days among workers in the energy sector by means of genetic algorithms |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2020-05-01 |
description |
In this research, a model is proposed for predicting the number of days absent from work due to sick or health-related leave among workers in the industry sector, according to ergonomic, social and work-related factors. It employs selected microdata from the Sixth European Working Conditions Survey (EWCS) and combines a genetic algorithm with Multivariate Adaptive Regression Splines (MARS). The most relevant explanatory variables identified by the model can be included in the following categories: ergonomics, psychosocial factors, working conditions and personal data and physiological characteristics. These categories are interrelated, and it is difficult to establish boundaries between them. Any managing program has to act on factors that affect the employees' general health status, process design, workplace environment, ergonomics and psychosocial working context, among others, to achieve success. This has an extensive field of application in the energy sector. |
topic |
sick leave absenteeism energy sector genetic algorithms (GA) multivariate adaptive regression splines (MARS) |
url |
https://www.mdpi.com/1996-1073/13/10/2475 |
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