A cloud-based LSTM–TabNet deep learning model for enhancing strategic HRM and employee performance prediction

Abstract

The growing demand for intelligent decision support in Strategic Human Resource Management (SHRM) drives the need for advanced predictive models for employee attrition and performance. This paper presents a hybrid deep learning framework combining TabNet and Long Short-Term Memory (LSTM) networks within a cloud-based architecture. TabNet’s attention-based feature selection and LSTM’s ability to model temporal relationships enable the model to effectively process both structured tabular data and time-series behavioural patterns. The model is trained and validated using the IBM HR Analytics Employee Attrition & Performance dataset, consisting of 1,470 employee records with 35 demographic and organizational features. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and regression metrics (R ², RMSE, MAE). Results show that the proposed TabNet–LSTM framework significantly outperforms traditional models, including Artificial Neural Networks, Reinforcement Learning, and Gradient Boosting, in terms of predictive accuracy and reliability. These findings highlight the potential of hybrid cloud-enabled deep learning systems to support proactive employee retention and performance optimization strategies. The original contribution of the study lies in the integration of interpretable tabular learning and sequential modelling within a scalable cloud-based HR analytics framework for enhanced strategic workforce decision-making.