An explainable artificial intelligence-based approach for prediction and analysis of mental health disorder due to work pressure in the tech industry
*Vaishali Mehta WadhwaCorresponding authordrvaishaliwadhwa@gmail.comDepartment of Computer Science and EngineeringPanipat Institute of Engineering and TechnologyPanipat, Haryana, 132101, India0000-0003-2017-0528View full profile → , Akanksha Mahajaner.aakanksha@yahoo.co.inDepartment of Computer Science and EngineeringPanipat Institute of Engineering and TechnologyPanipat, Haryana, 132101, India0000-0003-2775-7751View full profile → , Tamanna Jenatamannasinghdeo@gmail.comSilberman School of BusinessFairleigh Dickinson UniversityVancouver, V6B2P6, Canada0000-0003-2703-7420View full profile → , Jyoti Gargjyotigarg1055@gmail.comDepartment of Computer Science and EngineeringMaharishi Markandeswar Deemed to be UniversityAmbala, Haryana, 133203, India0009-0000-2762-3599View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 11 Dec 2024
- Published Online:
- 30 Sep 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2011
- Pages:
- 1821–1829
Abstract
Keywords
Subject Classifications
References
[1] V. Mehta, N. Batra, S. Goyal, A. Kaur, K. V. Dudekula, and G. J. Victor, “Machine learning based exploratory data analysis (EDA) and diagnosis of chronic kidney disease (CKD),” EAI Endorsed Transactions on Pervasive Health & Technology, vol. 10, no. 1 (2024).
[2] Puranam Revanth Kumar, B. Shilpa, Rajesh Kumar Jha “BrainTract: segmentation of white matter fiber tractography and analysis of structural connectivity using hybrid convolutional neural network”, Neuroscience, Vol. 580, pp. 218-230 (2025).
[3] T. He and N. Lia, “Age-structured model with varied contact patterns and vaccination of COVID-19 in Liaoning Province, China,” Journal of Statistics and Management Systems, vol. 27, no. 8, pp. 1525–1539 (2024).
[4] P. R. Kumar, R. K. Jha, P. A. Kumar, and B. D. Raju, “Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging,” Intelligent Medicine, vol. 4, no. 3, pp. 161–169 (2024).
[5] B. Chen, L. Wang, B. Li, and W. Liu, “Work stress, mental health, and employee performance,” Frontiers in Psychology, vol. 13 (Nov. 2022).
[6] P. R. Kumar, R. K. Jha, and P. A. Kumar, “BrainHyperintensities: Automatic segmentation of white matter hyperintensities in clinical brain MRI images using improved deep neural network,” The Journal of Supercomputing, vol. 80, pp. 15545–15581 (2024).
[7] K. Kavita, R. Kulkarni, A. H. Prasad, B. Jeyakumar, M. Thaile, and S. Gore, “A novel optimization-based blockchain technology using health care data for enhancing security and privacy in the medical system,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 8, pp. 2483–2494 (2024).
[8] P. R. Kumar, B. Shilpa, and R. K. Jha, “Brain disorders: Impact of mild SARS-CoV-2 may shrink several parts of the brain,” Neuroscience & Biobehavioral Reviews, vol. 149, pp. 105150 (2023).
[9] M. Fabietti, G. Di Lazzaro, A. A. Steixner-Kumar, J. Modolo, E. Boccardi, and A. Schulze-Bonhage, “Artifact detection in chronically recorded local field potentials using long-short term memory neural network,” in 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT), IEEE, pp. 1–6 (Oct. 2020).
[10] P. R. Kumar, R. K. Jha, and A. Katti, “Brain tissues segmentation in neurosurgery: A systematic analysis for quantitative tractography approaches,” Acta Neurologica Belgica, vol. 124, pp. 1–15 (2023).
[11] P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis, “Explainable AI: A review of machine learning interpretability methods,” Entropy, vol. 23, no. 1, pp. 18 (Dec. 2020).
[12] P. R. Kumar, B. Shilpa, R. K. Jha, and S. N. Mohanty, “A novel end-to-end approach for epileptic seizure classification from scalp EEG data using deep learning technique,” International Journal of Information Technology, vol. 15, pp. 4223–4231 (2023).
[13] M. B. Khan and A. K. J. Saudagar, “Comparative analysis of medical knowledge and inferential capabilities of modern LLMs,” Journal of Statistics and Management Systems, vol. 27, no. 8, pp. 1713–1722 (2024).
[14] T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial Intelligence, vol. 267, pp. 1–38 (Feb. 2019).
[15] P. R. Kumar, R. K. Jha, and P. A. Kumar, “Segmentation of White Matter Lesions in MRI Images Using Optimization-based Deep Neural Network,” in Proc. 4th Int. Conf. Image Process. Capsule Netw., pp. 253–267 (2023).
[16] K. N. Fitzgerald, R. R. Hodges, D. M. Hanes, M. Stack, D. Cheishvili, R. Szyf, and J. D. Bradley, “Potential reversal of epigenetic age using a diet and lifestyle intervention: A pilot randomized clinical trial,” Aging, vol. 13, no. 7, pp. 9419–9432 (Apr. 2021).
[17] P. R. Kumar, A. Katti, S. N. Mohanty, and S. N. Senapati, “A Deep Learning-based Approach for an Automated Brain Tumor Segmentation in MR Images,” Pattern Recognit. Data Anal. Appl., vol. 888, pp. 87–97 (2022).
[18] L. J. Reece, C. McInerney, J. Blazek, D. Foley, B. Schmutz, R. Bellew, and J. Brown, “Reducing financial barriers through the implementation of voucher incentives to promote children’s participation in community sport in Australia,” BMC Public Health, vol. 20, no. 1, pp. 19 (Dec. 2020).
