Unveiling mental health with machine learning and deep learning: Exploring applications and navigating challenges

Sandeep Kadam; Mukesh Kumar Tripathi; Chaitali Shewale; Priya Shelke; Pravin R. Futane; Suruchi Dedgaonkar

doi:10.31893/multiscience.2025250

Keywords:

mental health

prediction

support vector machine

machine learning and deep learning

Abstract

In today fast-paced society, psychological health issues such as anxiety, depression, and stress have become increasingly prevalent across diverse populations.This paper explores the prediction of anxiety, depression, and stress levels via machine learning algorithms. Five different machine learning algorithms were employed to predict the severity of anxiety, depression, and stress across the sampled population. These algorithms were chosen for their high accuracy, making them particularly well-suited for predicting psychological problems. However, applying these algorithms made it apparent that class imbalances existed in the confusion matrix, necessitating incorporating the F1 score measure. The F1 score proved crucial in identifying the most accurate model among the five applied algorithms, ultimately revealing that the random forest classifier was the most effective in predicting anxiety, depression, and stress levels. Moreover, the specificity parameter was examined, showing that the algorithms exhibited notable sensitivity to negative results. The authors emphasized the trade-off between accuracy and execution time, particularly in the hybrid approach. While the hybrid model combined the strengths of multiple algorithms to achieve higher prediction accuracy for anxiety, depression, and stress levels, it also resulted in longer execution times. This trade-off is a common challenge in machine learning, where increasing model complexity can lead to improved accuracy but at the cost of slower processing speeds. This study contributes to the growing body of research on machine learning in mental health prediction, offering valuable insights into the intricacies of class imbalances and the importance of performance metrics such as the f1 score and specificity. The findings underscore the potential of machine learning algorithms, particularly the random forest classifier, in enhancing our understanding and prediction of psychological health issues in diverse and dynamic populations.
References
1. Abbas, Z. K., & Al-Ani, A. A. (2022). Anomaly detection in surveillance videos based on H265 and deep learning. International Journal of Advanced Technology and Engineering Exploration, 9(92), 910.
2. Abdullah, F., Javeed, M., & Jalal, A. (2021, November). Crowd anomaly detection in public surveillance via spatio-temporal descriptors and zero-shot classifier. In 2021 International Conference on Innovative Computing (ICIC) (pp. 1-8). IEEE.
3. Shivendra, Chiranjeevi, K., & Tripathi, M. K. (2022). Detection of fruits image applying decision tree classifier techniques. In Computational Intelligence and Data Analytics: Proceedings of ICCIDA 2022 (pp. 127-139). Singapore: Springer Nature Singapore.
4. Pugazharasi, K., Kalaivani, P., Jayapriya, J., Kadhambari, S., & Mailvizhi, S. (2023, November). Machine learning algorithms for predicting depression, anxiety and stress in modern life. In AIP Conference Proceedings (Vol. 2587, No. 1). AIP Publishing.
5. Tripathi, M. K., & Shivendra. (2024). Neutrosophic approach based intelligent system for automatic mango detection. Multimedia Tools and Applications, 83(14), 41761-41783.
6. Doshi, K., & Yilmaz, Y. (2020). Continual learning for anomaly detection in surveillance videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 254-255).
7. Aleem, S., Huda, N. U., Amin, R., Khalid, S., Alshamrani, S. S., & Alshehri, A. (2022). Machine learning algorithms for depression: diagnosis, insights, and research directions. Electronics, 11(7), 1111.
8. Tripathi, M. K., Reddy, P. K., & Neelakantappa, M. (2023). Identification of mango variety using near infrared spectroscopy. Indonesian Journal of Electrical Engineering and Computer Science, 31(3), 1776-1783.
9. Lee, E. E., Torous, J., De Choudhury, M., Depp, C. A., Graham, S. A., Kim, H. C., ... & Jeste, D. V. (2021). Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 856-864.
10. Tripathi, M. K., Maktedar, D., Vasundhara, D. N., Moorthy, C. V. K. N. S. N., & Patil, P. (2023). Residual life assessment (RLA) analysis of apple disease based on multimodal deep learning model. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 1042-1050.
11. Lindemann, B., Maschler, B., Sahlab, N., & Weyrich, M. (2021). A survey on anomaly detection for technical systems using LSTM networks. Computers in Industry, 131, 103498.
12. Tripathi, M. K., Neelakantapp, M., Kaulage, A., Nabilal, K. V., Patil, S. N., & Bamane, K. D. (2023). Breast cancer image analysis and classification framework by applying machine learning techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 930-941.
13. Qasrawi, R., Polo, S. P. V., Al-Halawa, D. A., Hallaq, S., & Abdeen, Z. (2022). Assessment and prediction of depression and anxiety risk factors in schoolchildren: machine learning techniques performance analysis. JMIR Formative Research, 6(8), e32736.
14. Lavanya, P. V., Tripathi, M. K., E, P. H., & Ramesh, J. V. N. Hyperspectral crop image classification via ensemble of classification model with optimal training. In Web Intelligence (No. Preprint, pp. 1-31). IOS Press.
15. Trivedi, N. K., Tiwari, R. G., Witarsyah, D., Gautam, V., Misra, A., & Nugraha, R. A. (2022, November). Machine learning based evaluations of stress, depression, and anxiety. In 2022 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS) (pp. 1-5). IEEE.
16. Thieme, A., Belgrave, D., & Doherty, G. (2020). Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Transactions on Computer-Human Interaction (TOCHI), 27(5), 1-53.
17. Qasrawi, R., Amro, M., VicunaPolo, S., Al-Halawa, D. A., Agha, H., Seir, R. A., & Tayyem, R. (2022). Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study. F1000Research, 11.
18. Chancellor, S., Baumer, E. P., & De Choudhury, M. (2019). Who is the "human" in human-centered machine learning: The case of predicting mental health from social media. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-32.
19. Chung, J., & Teo, J. (2022). Mental health prediction using machine learning: taxonomy, applications, and challenges. Applied Computational Intelligence and Soft Computing, 2022(1), 9970363.
20. Filippis, R. D., & Foysal, A. A. (2024). Insights unveiled: Harnessing AI to explore human behaviour in social sciences. J Math Techniques Comput Math, 3(5), 1-08.
21. Tripathi, M. K., & Shivendra. (2024). Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach. Network: Computation in Neural Systems, 1-29.
22. Kumar, P., Garg, S., & Garg, A. (2020). Assessment of anxiety, depression and stress using machine learning models. Procedia Computer Science, 171, 1989-1998.
23. Tripathi, M. K., Hasini, S., Homamalini, M., & Neelakantappa, M. (2023, October). Pothole detection based on machine learning and deep learning models. In 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA) (pp. 1-9). IEEE.
24. Selva Mary, G., Blesswin, J., Venkatesan, M., Vairagar, S., Adagale, S., Shravage, C., & Barpute, J. (2023). Enhancing conversational sentimental analysis for psychological depression prediction with Bi-LSTM. Journal of Autonomous Intelligence, 7(1).
25. Wang, J., Cao, B., Yu, P., Sun, L., Bao, W., & Zhu, X. (2018, July). Deep learning towards mobile applications. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (pp. 1385-1393). IEEE.
26. Tripathi, M. K., Neelakantappa, M., Mahalle, P. N., Channapattana, S. V., Deshmukh, G., & Prashant, G. Detection and classification of mango fruit-based on feature extraction applying optimized hybrid LA-FF algorithms. In Data-Centric Artificial Intelligence for Multidisciplinary Applications (pp. 177-185). Chapman and Hall/CRC.
27. Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13(1), 23-47.
28. Albagmi, F. M., Alansari, A., Al Shawan, D. S., AlNujaidi, H. Y., & Olatunji, S. O. (2022). Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach. Informatics in Medicine Unlocked, 28, 100854.
29. Konduru, P., Tripathi, M. K., Khairnar, D., Patil, P., Patil, M., & Prashant, G. (2023, November). Emerging technology for smart farming in the agriculture domain: Application and future perspective. In 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) (pp. 88-93). IEEE.
30. Ebrahimi Araghizad, A., Tehranizadeh, F., Kilic, K., & Budak, E. (2023). Smart tool-related faults monitoring system using process simulation-based machine learning algorithms. Journal of Machine Engineering, 23.
31. Aydogmus, H. Y., & Turkan, Y. S. (2022). Application of machine learning methods for passenger demand prediction in transfer stations of Istanbul's public transportation system. In Research Anthology on Machine Learning Techniques, Methods, and Applications (pp. 1086-1106). IGI Global.
32. Adhikary, D. D., & Gupta, D. (2021). Applying over 100 classifiers for churn prediction in telecom companies. Multimedia Tools and Applications, 80(28), 35123-35144.
33. Rastogi, D., Johri, P., Tiwari, V., & Elngar, A. A. (2024). Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross-validation deep learning framework. Biomedical Signal Processing and Control, 88, 105602.
34. Sadeli, A. F., & Lawanda, I. I. (2023). Recall, precision, and F-measure for evaluating information retrieval system in electronic document management systems (EDMS). Khizanah al-Hikmah: Jurnal Ilmu Perpustakaan, Informasi, dan Kearsipan, 11(2), 231-241.
35. Ventriglio, A., Torales, J., Castaldelli-Maia, J. M., De Berardis, D., & Bhugra, D. (2021). Urbanization and emerging mental health issues. CNS Spectrums, 26(1), 43-50.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Kadam, S., Kumar Tripathi, M., Shewale, C., Shelke, P., Futane, P. R., & Dedgaonkar, S. (2024). Unveiling mental health with machine learning and deep learning: Exploring applications and navigating challenges. Multidisciplinary Science Journal, 7(5), 2025250. https://doi.org/10.31893/multiscience.2025250

[1] Abbas, Z. K., & Al-Ani, A. A. (2022). Anomaly detection in surveillance videos based on H265 and deep learning. International Journal of Advanced Technology and Engineering Exploration, 9(92), 910.

[2] Abdullah, F., Javeed, M., & Jalal, A. (2021, November). Crowd anomaly detection in public surveillance via spatio-temporal descriptors and zero-shot classifier. In 2021 International Conference on Innovative Computing (ICIC) (pp. 1-8). IEEE.

[3] Shivendra, Chiranjeevi, K., & Tripathi, M. K. (2022). Detection of fruits image applying decision tree classifier techniques. In Computational Intelligence and Data Analytics: Proceedings of ICCIDA 2022 (pp. 127-139). Singapore: Springer Nature Singapore.

[4] Pugazharasi, K., Kalaivani, P., Jayapriya, J., Kadhambari, S., & Mailvizhi, S. (2023, November). Machine learning algorithms for predicting depression, anxiety and stress in modern life. In AIP Conference Proceedings (Vol. 2587, No. 1). AIP Publishing.

[5] Tripathi, M. K., & Shivendra. (2024). Neutrosophic approach based intelligent system for automatic mango detection. Multimedia Tools and Applications, 83(14), 41761-41783.

[6] Doshi, K., & Yilmaz, Y. (2020). Continual learning for anomaly detection in surveillance videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 254-255).

[7] Aleem, S., Huda, N. U., Amin, R., Khalid, S., Alshamrani, S. S., & Alshehri, A. (2022). Machine learning algorithms for depression: diagnosis, insights, and research directions. Electronics, 11(7), 1111.

[8] Tripathi, M. K., Reddy, P. K., & Neelakantappa, M. (2023). Identification of mango variety using near infrared spectroscopy. Indonesian Journal of Electrical Engineering and Computer Science, 31(3), 1776-1783.

[9] Lee, E. E., Torous, J., De Choudhury, M., Depp, C. A., Graham, S. A., Kim, H. C., ... & Jeste, D. V. (2021). Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 856-864.

[10] Tripathi, M. K., Maktedar, D., Vasundhara, D. N., Moorthy, C. V. K. N. S. N., & Patil, P. (2023). Residual life assessment (RLA) analysis of apple disease based on multimodal deep learning model. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 1042-1050.

[11] Lindemann, B., Maschler, B., Sahlab, N., & Weyrich, M. (2021). A survey on anomaly detection for technical systems using LSTM networks. Computers in Industry, 131, 103498.

[12] Tripathi, M. K., Neelakantapp, M., Kaulage, A., Nabilal, K. V., Patil, S. N., & Bamane, K. D. (2023). Breast cancer image analysis and classification framework by applying machine learning techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 930-941.

[13] Qasrawi, R., Polo, S. P. V., Al-Halawa, D. A., Hallaq, S., & Abdeen, Z. (2022). Assessment and prediction of depression and anxiety risk factors in schoolchildren: machine learning techniques performance analysis. JMIR Formative Research, 6(8), e32736.

[14] Lavanya, P. V., Tripathi, M. K., E, P. H., & Ramesh, J. V. N. Hyperspectral crop image classification via ensemble of classification model with optimal training. In Web Intelligence (No. Preprint, pp. 1-31). IOS Press.

[15] Trivedi, N. K., Tiwari, R. G., Witarsyah, D., Gautam, V., Misra, A., & Nugraha, R. A. (2022, November). Machine learning based evaluations of stress, depression, and anxiety. In 2022 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS) (pp. 1-5). IEEE.

[16] Thieme, A., Belgrave, D., & Doherty, G. (2020). Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Transactions on Computer-Human Interaction (TOCHI), 27(5), 1-53.

[17] Qasrawi, R., Amro, M., VicunaPolo, S., Al-Halawa, D. A., Agha, H., Seir, R. A., & Tayyem, R. (2022). Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study. F1000Research, 11.

[18] Chancellor, S., Baumer, E. P., & De Choudhury, M. (2019). Who is the "human" in human-centered machine learning: The case of predicting mental health from social media. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-32.

[19] Chung, J., & Teo, J. (2022). Mental health prediction using machine learning: taxonomy, applications, and challenges. Applied Computational Intelligence and Soft Computing, 2022(1), 9970363.

[20] Filippis, R. D., & Foysal, A. A. (2024). Insights unveiled: Harnessing AI to explore human behaviour in social sciences. J Math Techniques Comput Math, 3(5), 1-08.

[21] Tripathi, M. K., & Shivendra. (2024). Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach. Network: Computation in Neural Systems, 1-29.

[22] Kumar, P., Garg, S., & Garg, A. (2020). Assessment of anxiety, depression and stress using machine learning models. Procedia Computer Science, 171, 1989-1998.

[23] Tripathi, M. K., Hasini, S., Homamalini, M., & Neelakantappa, M. (2023, October). Pothole detection based on machine learning and deep learning models. In 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA) (pp. 1-9). IEEE.

[24] Selva Mary, G., Blesswin, J., Venkatesan, M., Vairagar, S., Adagale, S., Shravage, C., & Barpute, J. (2023). Enhancing conversational sentimental analysis for psychological depression prediction with Bi-LSTM. Journal of Autonomous Intelligence, 7(1).

[25] Wang, J., Cao, B., Yu, P., Sun, L., Bao, W., & Zhu, X. (2018, July). Deep learning towards mobile applications. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (pp. 1385-1393). IEEE.

[26] Tripathi, M. K., Neelakantappa, M., Mahalle, P. N., Channapattana, S. V., Deshmukh, G., & Prashant, G. Detection and classification of mango fruit-based on feature extraction applying optimized hybrid LA-FF algorithms. In Data-Centric Artificial Intelligence for Multidisciplinary Applications (pp. 177-185). Chapman and Hall/CRC.

[27] Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13(1), 23-47.

[28] Albagmi, F. M., Alansari, A., Al Shawan, D. S., AlNujaidi, H. Y., & Olatunji, S. O. (2022). Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach. Informatics in Medicine Unlocked, 28, 100854.

[29] Konduru, P., Tripathi, M. K., Khairnar, D., Patil, P., Patil, M., & Prashant, G. (2023, November). Emerging technology for smart farming in the agriculture domain: Application and future perspective. In 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) (pp. 88-93). IEEE.

[30] Ebrahimi Araghizad, A., Tehranizadeh, F., Kilic, K., & Budak, E. (2023). Smart tool-related faults monitoring system using process simulation-based machine learning algorithms. Journal of Machine Engineering, 23.

[31] Aydogmus, H. Y., & Turkan, Y. S. (2022). Application of machine learning methods for passenger demand prediction in transfer stations of Istanbul's public transportation system. In Research Anthology on Machine Learning Techniques, Methods, and Applications (pp. 1086-1106). IGI Global.

[32] Adhikary, D. D., & Gupta, D. (2021). Applying over 100 classifiers for churn prediction in telecom companies. Multimedia Tools and Applications, 80(28), 35123-35144.

[33] Rastogi, D., Johri, P., Tiwari, V., & Elngar, A. A. (2024). Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross-validation deep learning framework. Biomedical Signal Processing and Control, 88, 105602.

[34] Sadeli, A. F., & Lawanda, I. I. (2023). Recall, precision, and F-measure for evaluating information retrieval system in electronic document management systems (EDMS). Khizanah al-Hikmah: Jurnal Ilmu Perpustakaan, Informasi, dan Kearsipan, 11(2), 231-241.

[35] Ventriglio, A., Torales, J., Castaldelli-Maia, J. M., De Berardis, D., & Bhugra, D. (2021). Urbanization and emerging mental health issues. CNS Spectrums, 26(1), 43-50.

Abstract

References

How to cite