With the increasing use of artificial intelligence in decision-making systems, predicting employee performance has attracted growing attention in human resource analytics. This study aims to systematically evaluate the impact of data preprocessing and model optimization techniques on artificial neural network (ANN)-based prediction of employee performance in HR analytics. Three publicly available HR datasets were used, and multiple configurations involving feature selection, feature extraction, principal component analysis (PCA), reduced architectures, and regularization were evaluated. The experimental results show that appropriate feature selection and regularization consistently improve predictive performance across datasets, whereas PCA-based dimensionality reduction resulted in lower accuracy in the evaluated datasets, possibly due to the loss of discriminative information. Additionally, simplified ANN architectures yielded modest, but consistent improvements in generalization performance across datasets, highlighting the importance of controlling model complexity. The top-performing configurations across the assessed datasets achieved accuracies ranging from 81% to 96%. These findings offer practical guidance on selecting efficient preprocessing and architectural techniques when applying ANN-based models in human resource analytics.
Bajhzer et al. (Thu,) studied this question.