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

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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)

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.

Suggested Citation

  • Aroa González Fuentes & Nélida M. Busto Serrano & Fernando Sánchez Lasheras & Gregorio Fidalgo Valverde & Ana Suárez Sánchez, 2020. "Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms," Energies, MDPI, vol. 13(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2475-:d:358031
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    References listed on IDEAS

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    1. Damir Jakus & Rade Čađenović & Josip Vasilj & Petar Sarajčev, 2020. "Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm," Energies, MDPI, vol. 13(7), pages 1-21, March.
    2. Javier DE ANDRES & Fernando SÁNCHEZ-LASHERAS & Pedro LORCA & Francisco Javier DE COS JUEZ, 2011. "A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 10(3), pages 351-374, September.
    3. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
    4. Krzemień, Alicja, 2019. "Fire risk prevention in underground coal gasification (UCG) within active mines: Temperature forecast by means of MARS models," Energy, Elsevier, vol. 170(C), pages 777-790.
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    Cited by:

    1. Fernando Sánchez Lasheras, 2021. "Predicting the Future-Big Data and Machine Learning," Energies, MDPI, vol. 14(23), pages 1-2, December.

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