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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Main Authors: Aroa González Fuentes, Nélida M. Busto Serrano, Fernando Sánchez Lasheras, Gregorio Fidalgo Valverde, Ana Suárez Sánchez
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/13/10/2475
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spelling 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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