@ARTICLE{Perez-Campdesuner_Reyner_Incidences_2020,
 author={Perez-Campdesuner, Reyner and De Miguel-Guzan, Margarita and Garcıa-Vidal, Gelmar and Sanchez-Rodrıguez, Alexander and Martınez-Vivar, Rodobaldo},
 volume={vol. 11},
 number={No 1},
 journal={Management and Production Engineering Review},
 howpublished={online},
 year={2020},
 publisher={Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management},
 abstract={Labor absenteeism is a factor that affects the good performance of organizations in any part of the world, from the instability that is generated in the functioning of the system. This is evident in the effects on quality, productivity, reaction time, among other aspects. The direct causes by which it occurs are generally known and with greater reinforcement the diseases are located, without distinguishing possible classifications. However, behind these or other causes can be found other possible factors of incidence, such as age or sex. This research seeks to explore, through the application of neural networks, the possible relationship between different variables and their incidence in the levels of absenteeism. To this end, a neural networks model is constructed from the use of a population of more than 12,000 employees, representative of various classification categories. The study allowed the characterization of the influence of the different variables studied, supported in addition to the performance of an ANOVA analysis that allowed to corroborate and clarify the results of the neural network analysis.},
 title={Incidences of variables in labor absenteeism: an analysis of neural networks},
 URL={http://czasopisma.pan.pl/Content/116160/PDF/1-355-kolor.pdf},
 doi={10.24425/mper.2020.132938},
 keywords={Labor absenteeism, neural networks model, human resource management, ANOVA analysis},
}