Author(s):
Narendra Kumar Dewangan, Abhishek Anand, Gunjan Chhabra, Nitin Ajabrao Dhawas, Imran Ibrahim Sayyad
Email(s):
narendra.nic@gmail.com
DOI:
10.52711/0974-360X.2026.00264 
Address:
Narendra Kumar Dewangan1, Abhishek Anand2, Gunjan Chhabra3, Nitin Ajabrao Dhawas4, Imran Ibrahim Sayyad5
1Asst. Professor, Department of AIML, SSIPMT Raipur India.
2Department of Pharmacy Practice, Teerthanker Mahaveer College of Pharmacy, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India -244001.
3Dept of Computer Science and Engineering, School of Science and Technology, Swami Rama Himalayan University, Dehradun, Uttarakhand, India.
4Department of Electronics and Telecommunication Engineering, Nutan Maharashtra Institute of Engineering and Technology, Pune Pin 410507, Maharashtra, India.
5Department of Mechanical Engineering, Assistant Professor, Sanjivani College of Engineering, Affiliated to Savitribai Phule Pune University, Pune. Maharashtra, India.
*Corresponding Author
Published In:
Volume - 19,
Issue - 4,
Year - 2026
ABSTRACT:
Millions of people of all ages and socioeconomic backgrounds are impacted by mental health illnesses, which are becoming a major global concern. Early detection and intervention are crucial in managing these conditions effectively. In generic diagnostic procedure often rely on subjective assessments and clinical interviews, which may delay diagnosis and treatment, In machine learning (ML), there is increasing potential to invent the predictive procedure that can recognize individuals at cause of mental health issues using behavioral, physiological, and demographic data. A thorough investigation into the use of machine learning methods for the early detection of mental health conditions such bipolar disorder, anxiety, and depression is presented in this research report. A systematic literature review is conducted to evaluate existing models and datasets. To improve prediction accuracy, a unique approach that combines ensemble learning, feature selection, and data preparation is put forth. Real-world datasets sourced from public repositories are used to assess the performance of several machine learning methods, such as Random Forest, Support Vector Machine (SVM), Logistic Regression, and Gradient Boosting. The findings show that ensemble approaches—in particular, XGBoost—perform better, achieving 94.3% accuracy, 92.7% sensitivity, and 95.1% specificity. The results highlight how machine learning might help with early diagnosis and individualized mental health treatment. This study offers a foundation for further research and therapeutic applications while also adding to the expanding corpus of knowledge in computational psychiatry.
Cite this article:
Narendra Kumar Dewangan, Abhishek Anand, Gunjan Chhabra, Nitin Ajabrao Dhawas, Imran Ibrahim Sayyad. Predictive Modeling of Mental Health Conditions Using Machine Learning Algorithms. Research Journal of Pharmacy and Technology. 2026;19(4):1842-8. doi: 10.52711/0974-360X.2026.00264
Cite(Electronic):
Narendra Kumar Dewangan, Abhishek Anand, Gunjan Chhabra, Nitin Ajabrao Dhawas, Imran Ibrahim Sayyad. Predictive Modeling of Mental Health Conditions Using Machine Learning Algorithms. Research Journal of Pharmacy and Technology. 2026;19(4):1842-8. doi: 10.52711/0974-360X.2026.00264 Available on: https://rjptonline.org/AbstractView.aspx?PID=2026-19-4-53
5. REFERENCES:
1. Henderson, C., Evans-Lacko, S., and Thornicroft, G. Mental health stigma and help-seeking: A systematic review. Health and Social Care in the Community. 2013; 21(1): 1–12.
2. Corrigan, P. W. How stigma interferes with mental health care. American Psychologist. 2004; 59(7): 614–625.
3. Andrade, L. H., Alonso, J., Mneimneh, Z., Wells, J. E., Al-Hamzawi, A., Borges, G. and Kessler, R. C. (2014).
4. Kroenke, K., Spitzer, R. L., and Williams, J. B. The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine. 2001;16(9): 606–613.
5. Insel, T. R. The dilemma of DSM-5. JAMA. 2014; 310(20): 2129–2130.
6. Wang, P. S., Aguilar-Gaxiola, S., Alonso, J., Angermeyer, M. C., Borges, G., Bromet, E. J., and Kessler, R. C. Delay and failure in treatment seeking after first onset of mental disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry. 2007; 64(11): 1279–1286.
7. Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., and Walters, E. E. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry. 2005; 62(6): 593–602.
8. Torous, J., Nicholas, J., Larsen, M. E., Firth, J., and Christensen, H. Digital phenotyping for mental health: A review. NPJ Digital Medicine. 2020; 3(1): 1–8. https://doi.org/10.1038/s41746-020-00363-8
9. Shatte, A. B., Hutchinson, D. M., and Teague, S. J. Machine learning in mental health: A scoping review. JMIR Medical Informatics. 2019; 7(2): e12729.
10. Saeb, S., Zhang, M., Kwasny, M. J., Karr, C. J., Kording, K. P., and Mohr, D. C. Mobile phone sensing for mental health. JMIR Mental Health. 2015; 2(2): e15.
11. De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. Predicting depression via social media. Proceedings of the International AAAI Conference on Web and Social Media. 2013; 7(1): 1–10.
12. Alharbi, Y., Alshahrani, S., and Househ, M. (). Depression detection using machine learning and PHQ-9 questionnaire. Healthcare Informatics Research. 2021; 27(1): 45–52.
13. Insel, T. R. Digital phenotyping: Technology for a new science of behavior. JAMA. 2017; 318(13): 1215–1216.
14. Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., and Bartels, S. J. Digital technology for mental health. Current Opinion in Psychology. 2020; 36: 109–115.
15. Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P., and Haines, A. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: A systematic review. PLoS Medicine. 2013; 10(1): e1001362.
16. Montoya, A., Ho, N., and Insel, T. R. Computational psychiatry: New perspectives on mental illness. Annual Review of Clinical Psychology. 2021; 17: 1–25.
17. Reece, A. G., and Danforth, C. M. Instagram photos reveal predictive markers of depression. EPJ Data Science. 2017; 6(1): 1–12.
18. Coppersmith, G., Dredze, M., and Harman, C. (2014). Quantifying mental health signals in Twitter. Proceedings of the Workshop on Computational Linguistics and Clinical Psychology. (pp. 51–60).
19. Seker, D., Alshahrani, M., and Alzahrani, A. BERT-based detection of suicidal ideation in social media. IEEE Access. 2020; 8: 156747–156756.
20. Alvarez-Melis, D., and Shah, N. D. Clinician-patient dialogue as a data source for machine learning in mental health. Journal of Biomedical Informatics. 2018; 86: 112–121.
21. Reddy, M. P., Jabeen, S., and Reddy, M. S. Machine learning approach for detection of depression using EHR data. Journal of Medical Systems. 2020; 44(5): 1–9.
22. Chen, Y., Reddy, M. B., and Reddy, M. S. Predicting anxiety using EHR data and machine learning. Journal of Medical Systems. 2018; 42(11): 1–8.
23. Koppel, M., Schler, J., and Bonchek-Dokow, E. Automated detection of bipolar disorder from clinical notes. Journal of Biomedical Informatics. 2019; 90: 103098.
24. Singh, R., Wang, Y., and Chen, X. Hybrid deep learning for depression prediction from EHRs. Artificial Intelligence in Medicine. 2021; 112: 102034.
25. Wang, Y., Chen, Z., and Liu, Y. Federated learning for mental health prediction. Nature Communications. 2020; 11(1): 1–10.
26. Shatte, A. B., Hutchinson, D. M., and Teague, S. J. Machine learning in mental health: A scoping review. JMIR Medical Informatics. 2019; 7(2): e12729.
27. Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt-Jepsen, M., and Firth, J. The growing field of digital mental health. JAMA Psychiatry. 2021; 78(5): 455–456.
28. Birnbaum, M. L., Rizvi, A. F., Correll, C. U., Kane, J. M., and Bucci, S. Passive sensing for mental health: A review of smartphone-based monitoring. Current Psychiatry Reports. 2020; 22(8): 1–9.
29. Schnyer, D. M., Clasen, P., and Beevers, C. G. fMRI-based prediction of depression using machine learning. NeuroImage: Clinical. 2017; 15: 575–584.
30. Drysdale, A. T., Dunlop, B. W., Downar, J., Fox, M., Iosifescu, D., Nemeroff, C. B., and Williams, L. M. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine. 2017; 23(1): 28–38.
31. Ghandeharioun, A., Azaria, A., Shen, L., and Picard, R. W. Detecting depression with audio and facial recognition. IEEE Transactions on Affective Computing. 2019; 10(3): 330–342.
32. Tao, D., Li, X., Liu, W., and Zhang, Z. Multimodal fusion of EEG, facial video, and electronic health records for improved depression prediction. Journal of Affective Disorders. 2020; 274: 1025–1033.
33. Zhou, Y., Zhang, H., Liu, J., and Wang, L. Predicting antidepressant treatment response using graph neural networks on functional brain connectivity. NeuroImage: Clinical. 2021; 32: 102845.
34. Chen, T., and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (pp. 785–794).
35. Meenakshi K, Safa M, Karthick T, Sivaranjani N. A Novel Study of Machine Learning Algorithms for Classifying Health Care Data. Research J. Pharm. and Tech. 2017; 10(5): 1429-1432.
36. Leo DencelinX, Ramkumar T. Distributed Machine Learning Algorithms to classify Protein secondary structures for Drug Design – A Survey. Research J. Pharm. and Tech. 2017; 10(9): 3173-3180.
37. P. Grace Kanmani Prince, Rani Hemamalini, U. Anitha, J. Premalatha, K. Sudheera. Detection of seizure using EEG Signals by Supervised Learning Algorithms. Research J. Pharm. and Tech 2017; 10(10): 3443-3448.
38. Naresh. K, Prabakaran. N, Kannadasan. R, Boominathan. P. Diabetic Medical Data Classification using Machine Learning Algorithms. Research J. Pharm. and Tech. 2018; 11(1): 97-100
39. Shyamala Devi M, Sruthi A. N, Saranya Jothi C. MRI Liver Tumor Classification Using Machine Learning Approach and Structure Analysis. Research J. Pharm. and Tech 2018; 11(2): 434-438.
40. Basil K Varghese, Geraldine Bessie Amali D, Uma Devi K S. Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech dataset. Research J. Pharm. and Tech 2019; 12(2): 644-648.
41. R. M. Balajee, K. Venkatesh. A Survey on Machine Learning Algorithms and finding the best out there for the considered seven Medical Data Sets Scenario. Research J. Pharm. and Tech. 2019; 12(6): 3059-3062.
42. Vikas Chaurasia, Saurabh Pal. Skin Diseases Prediction: Binary Classification Machine Learning and Multi Model Ensemble Techniques. Research J. Pharm. and Tech 2019; 12(8): 3829-3832.
43. Prachi Parvatikar, Joy Hoskeri, Bhagirathi Hallali, Kusal K Das. Proteochemometric (PCM) Modelling: A Machine Learning Technique for Drug Designing. Research Journal of Pharmacy and Technology. 2024; 17(3): 1382-5.
44. Adnan R. Ahmad. Chemical Reaction Prediction using Machine Learning. Research Journal of Pharmacy and Technology. 2024; 17(11): 5435-8.
45. Suprapto Suprapto, Yatim Lailun Ni’mah. Classification of Dopamine D2 receptor ligands using RDKit Molecular descriptors and Machine Learning Algorithms. Research Journal of Pharmacy and Technology. 2024; 17(9): 4507-4.