Predictive analysis of health-related absences among employees of the Regional Electoral Court of Rio Grande do Norte using LSTM neural networks (Research, Society and Development, 2025)
The Electoral Justice system is characterized by peaks in demand during election years and reduced demand in non-election years, requiring strategic human resource planning with internal staff reassignments during critical periods. In this context, this study aimed to predict health-related absences at the Regional Electoral Court of Rio Grande do Norte (TRE-RN), identifying patterns to support decisions on staff reinforcement during critical periods, based on the analysis of 15 years of medical leave data (from 2010 to 2024). The methodology involved analyzing absence data using a Long Short-Term Memory (LSTM) neural network implemented with the PyTorch library in Python. The results demonstrated the effectiveness of the LSTM model in forecasting, achieving a Mean Absolute Error (MAE) of 3.14 and a Root Mean Square Error (RMSE) of 4.05, outperforming the traditional SARIMAX model. This research contributes to human resource management at TRE-RN by supporting decisions on staff allocation during election periods and providing additional insights for monitoring occupational health conditions by the sector responsible for employee healthcare.