Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms

Aroa González Fuentes, Nélida M. Busto Serrano, Fernando Sánchez Lasheras, Gregorio Fidalgo Valverde and Ana Suárez Sánchez
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Aroa González Fuentes: School of Mining, Energy and Materials Engineering of Oviedo, University of Oviedo, 33007 Oviedo, Spain
Nélida M. Busto Serrano: Labor and Social Security Inspectorate, Ministry of Labor and Social Economy, 33007 Oviedo, Spain
Fernando Sánchez Lasheras: Mathematics Department, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain
Gregorio Fidalgo Valverde: Department of Business Administration, School of Mining, Energy and Materials Engineering of Oviedo, University of Oviedo, 33007 Oviedo, Spain
Ana Suárez Sánchez: Department of Business Administration, School of Mining, Energy and Materials Engineering of Oviedo, University of Oviedo, 33007 Oviedo, Spain

Energies, 2020, vol. 13, issue 10, 1-16

Abstract: 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.

Keywords: sick leave; absenteeism; energy sector; genetic algorithms (GA); multivariate adaptive regression splines (MARS) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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