A cluster-based human resources analytics for predicting employee turnover using optimized Artificial Neural Networks and data augmentation

Abstract

This study presents an innovative methodology to predict employee turnover by integrating Artificial Neural Networks (ANN) with clustering techniques. We focus on hyperparameter tuning with various input parameters to obtain optimal ANN models. By segmenting data, the study identifies critical turnover predictors, allowing targeted interventions to be implemented to improve the efficiency and effectiveness of retention policies. Data augmentation using Conditional Generative Adversarial Networks (CTGAN) is performed on clusters with imbalanced data. Following this, the optimized ANN models are applied to these augmented clusters, leading to a notable improvement in their performance. We evaluate our optimized ANN models against five other ANN variants and four traditional machine learning models to demonstrate their superior accuracy and recall. The proposed approach achieves operational advantages by shifting away from generalized strategies to more focused, cluster-based policies, which can optimize resource utilization and reduce costs. Because of its practicality and enhanced ability to predict and manage employee turnover, this method, supported by empirical evidence, is a significant advancement in human resource (HR) analytics

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