This research work aims to develop a tool to identify employees who might abandon their position, because job abandonment is considered an international problem. The proposed method consists of a genetic algorithm that allows identifying the significant variables and improving the architecture of an artificial neural network as a solution. The variables selected by the tool were similar to those collected from different studies but not all of them were considered in such studies (e.g., distance between home and workplace, and years of employment). Likewise, the variables and architecture selected by the tool allowed to predict job abandonment up to 88.92 % accuracy rate.

This research work aims to develop a tool to identify employees who might abandon their position, because job abandonment is considered an international problem. The proposed method consists of a genetic algorithm that allows identifying the significant variables and improving the architecture of an artificial neural network as a solution. The variables selected by the tool were similar to those collected from different studies but not all of them were considered in such studies (e.g., distance between home and workplace, and years of employment). Likewise, the variables and architecture selected by the tool allowed to predict job abandonment up to 88.92 % accuracy rate.