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.