An Enhanced Employee Promotion Prediction System Using a Backpropagation-Artificial Neural Network Approach

Abstract

This study focuses on enhancing employee promotion prediction systems, addressing challenges such as class imbalance and the inefficiencies of traditional evaluation methods. The goal is to develop a predictive model that accurately identifies employees eligible for promotion while ensuring fairness and minimizing bias. To achieve this, the study employs a Backpropagation Artificial Neural Network (BP-ANN) model, trained on a dataset of 54,808 samples sourced from a multinational company and pre-processed using techniques such as normalization and feature selection. This approach mitigates the challenges of data imbalance by employing Synthetic Minority Over-sampling Technique (SMOTE) and cost-sensitive learning. Additionally, the study incorporates advanced evaluation metrics, including precision, recall, and F1-score, to assess the model's robustness and effectiveness. The proposed BP-ANN model outperforms existing systems, achieving a training accuracy of 96.3% and a validation accuracy of 91.1%, along with a precision and recall of 94% and 93%, respectively. These results highlight the potential of neural networks in revolutionizing employee promotion systems, and future research should focus on deploying the model in real-world organizational settings for broader applicability.