HDLF: Hybrid Deep Learning Framework of DNN and LSTM for Workforce Sustainability
Received: 9 June 2025 | Revised: 21 June 2025 and 9 July 2025 | Accepted: 11 July 2025 | Online: 21 August 2025
Corresponding author: Chaya J. Swamy
Abstract
Workforce sustainability has become a critical concern for organizations striving to maintain long-term productivity, employee well-being, and operational resilience. This paper presents a Hybrid Deep Learning Framework (HDLF) that integrates Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) to model and predict key workforce sustainability indicators. The proposed architecture leverages the DNN's strength in capturing complex, nonlinear relationships within multidimensional workforce data, while the LSTM component effectively learns temporal patterns from sequential records in monthly burnout scores, job satisfaction, workload indices, and remote workdays. Using a Workforce Sustainability and Retention Study dataset (January-December 2024) comprising 830 complete records from six Indian IT organizations, HDLF was evaluated on Retention Intent Prediction (binary classification) and Burnout Risk Prediction (multi-class classification). Quantitative results show that the proposed HDLF achieved superior performance over Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and single-branch neural networks. For retention prediction, it achieved an accuracy of 91%, a precision of 90%, a recall of 93%, an F1-score of 91%, and a ROC-AUC of 0.94. For burnout risk prediction, it achieved macro-averaged accuracy of 88%, precision of 86%, recall of 87%, and F1-score of 86%. Confusion matrices indicate improved detection of "At Risk" and "High Burnout" employees, critical for HR interventions, while ROC-AUC confirms strong class separability. The proposed framework demonstrates scalability and reliability, with future work focusing on real-time analytics, cross-industry datasets, and explainable AI for transparent HR decision-making.
Keywords:
work-life balance, IT sector, remote work, employee retention, burnout, gender differencesDownloads
References
K. M. Chethana and S. D. Noronha, "A Review of the Strategies Used in the IT sector for Employee Training and Development," International Journal of Case Studies in Business, IT, and Education, pp. 141–155, Apr. 2023.
Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
S. S. Haykin, Neural Networks and Learning Machines. Pearson, 2009.
S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
F. Karim, S. Majumdar, H. Darabi, and S. Chen, "LSTM Fully Convolutional Networks for Time Series Classification," IEEE Access, vol. 6, pp. 1662–1669, 2018.
S. Aggarwal, M. Singh, S. Chauhan, M. Sharma, and D. Jain, "Employee Attrition Prediction Using Machine Learning Comparative Study," in Intelligent Manufacturing and Energy Sustainability, vol. 265, A. N. R. Reddy, D. Marla, M. N. Favorskaya, and S. C. Satapathy, Eds. Springer Singapore, 2022, pp. 453–466.
T. Thulasimani, L. Saha, B. Poreddy, V. Karimli Maharram, V. Prabakaran, and A. Kumar Lamba, "Advanced Analysis of Social and Psychological Factors in Higher Education Institutions Using DNN and LSTM," in 2024 International Conference on Data Science and Network Security (ICDSNS), Tiptur, India, Jul. 2024, pp. 1–6.
H. Dehghan Shoorkand, M. Nourelfath, and A. Hajji, "A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning," Reliability Engineering & System Safety, vol. 241, Jan. 2024, Art. no. 109707.
W. Li, "A transformer-based deep learning framework to predict employee attrition," PeerJ Computer Science, vol. 9, Sep. 2023, Art. no. e1570.
W. G. Alheadary, "Controlling Employability Issues of Computing Graduates through Machine Learning-Based Detection and Identification," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10888–10894, Jun. 2023.
C. Saha, P. Chakraborty, P. C. Talukder, Md. Tofazzal Hosen, Md. Mohi Uddin, and M. A. Yousuf, "Employee Attrition Prediction using Ensemble Methods," in Proceedings of Third International Conference on Computing and Communication Networks, vol. 917, G. Fortino, A. Kumar, A. Swaroop, and P. Shukla, Eds. Springer Nature Singapore, 2024, pp. 481–493.
E. Oparina et al., "Machine learning in the prediction of human wellbeing," Scientific Reports, vol. 15, no. 1, Jan. 2025, Art. no. 1632.
P. Sharma, S. Bhattacharya, and S. Bhattacharya, "HR analytics and AI adoption in IT sector: reflections from practitioners," Journal of Work-Applied Management, Feb. 2025.
U. Murugesan, P. Subramanian, S. Srivastava, and A. Dwivedi, "A study of Artificial Intelligence impacts on Human Resource Digitalization in Industry 4.0," Decision Analytics Journal, vol. 7, Jun. 2023, Art. no. 100249.
F. Justus T and B. Vaishnavi, "A Study on Flexible Work Arrangements and its Impact on Employees Job Satisfaction Level with Reference to Selected Information Technology Organizations at Bengaluru City," Indian Journal of Natural Sciences, vol. 14, no. 81, pp. 65856–65864, Dec. 2023.
S. Alduayj and K. Rajpoot, "Predicting Employee Attrition using Machine Learning," in 2018 International Conference on Innovations in Information Technology (IIT), Al Ain, United Arab Emirates, Nov. 2018, pp. 93–98.
S. D. Paigude and S. R. Shikalgar, "Deep Learning Model for Work-Life Balance Prediction for Working Women in IT Industry," in Proceedings of the 4th International Conference on Information Management & Machine Intelligence, Jaipur, India, Dec. 2022, pp. 1–8.
R. Arun, M. Umamaheswari, K. Premalatha, M. V. Kumar, A. Stella, and Swerna. Pl, "Stress Management Through Workplace Associations with Productivity and Mood: The Impact of Learning Experience Based on Hybrid RF-GA-DNN Approach," in 2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC), Bengaluru, India, May 2024, pp. 1–6.
A. Gálvez, F. Tirado, and M. J. Martínez, "Work–Life Balance, Organizations and Social Sustainability: Analyzing Female Telework in Spain," Sustainability, vol. 12, no. 9, Apr. 2020, Art. no. 3567.
S. Garg, S. Sinha, A. K. Kar, and M. Mani, "A review of machine learning applications in human resource management," International Journal of Productivity and Performance Management, vol. 71, no. 5, pp. 1590–1610, Feb. 2021.
E. S. P. Sondhi, Dr. V. Sandhu, and Dr. S. Goyal, "Determinant Analysis and Effect of Variables on Work Life Balance of PSPCL Employees," International Journal of Psychosocial Rehabilitation, vol. 24, no. 04, pp. 3136–3147, Feb. 2020.
L. Mwangi, C. C. Boinett, E. Tumwet, and D. Bowen, "Effects of Work life Balance on Employees Performance in Institutions of Higher Learning: A Case Study of Kabarak University," Kabarak Journal of Research & Innovation, vol. 4, no. 2, pp. 60–69, 2016.
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