AI-Driven Detection of Depressive Disorders: A Machine Learning Approach

Authors

  • Sonia Sodhi Department of Computer Science, Rajdhani College, University of Delhi, India
  • Raj Kumar Sharma Department of Computer Science and Engineering, KK Modi University, Chhattisgarh, India

DOI:

https://doi.org/10.48165/tjmitm.2024.15.09

Keywords:

ANN, SVM, MLR, Depression; healthcare

Abstract

This research centres on the identification of patients experiencing depression. To identify the condition, this paper suggests three machine learning methodologies: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Multi Linear Regression (MLR) models. The primary aim was pinpointing the most efficient model for accurately recognising depression. Following a thorough evaluation, the ANN model displayed superior performance among the trio, achieving 100% accuracy in depression identification. The ANN model’s architecture, comprising one input, hidden, and output layer, proved to be a better fit for the data, resulting in fewer errors than the SVM and MLR models. The study utilised accuracy and loss graphs from training and validation datasets and a confusion matrix to evaluate model efficacy. The results confirm that the ANN model surpasses the others, particularly regarding cross-entropy loss. These findings highlight the ANN model’s potential usefulness in sending timely text messages or alerts to healthcare professionals based on the patient’s condition and treatment needs.

References

1. Su, D., Zhang, X., He, K., & Chen, Y. (2021). Use of machine learning approach to predict depression in the elderly in Chi na: A longitudinal study. Journal of Affective Disorders, 282, 289-298.

2. Nickson, D., Meyer, C., Walasek, L., & Toro, C. (2023). Predict ing depression using electronic health records data: A system atic review.

3. Dash, D. P., Kolekar, M. H., Chakraborty, C., & Khosravi, M. R. (2022). Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis. Transactions on Asian and Low-Resource Language Information Processing.

4. David, D. S., & Samraj, M. (2020). A comprehensive survey of emotion recognition system in facial expression. Artech J. Eff. Res. Eng. Technol, 1, 76-81.

5. Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., & Sikdar, B. (2021). A review on the role of machine learning in enabling IoT based healthcare ap plications. IEEE Access, 9, 38859-38890.

6. Sadasivuni, S. T. (2021). Analyzing Tweets For Predicting Men tal Health States Using Data Mining And Machine Learning Algorithms.

7. D’Hotman, D., & Loh, E. (2020). AI enabled suicide prediction tools: a qualitative narrative review. BMJ health & care infor matics, 27(3).

8. Moriarty, A. S., Meader, N., Snell, K. I., Riley, R. D., Paton, L. W.,

Chew-Graham, C. A., ... & McMillan, D. (2021). Prognostic models for predicting relapse or recurrence of major depres sive disorder in adults. Cochrane Database of Systematic Re views, (5).

9. Aleem, S., Huda, N. U., Amin, R., Khalid, S., Alshamrani, S. S., & Alshehri, A. (2022). Machine learning algorithms for depres sion: diagnosis, insights, and research directions. Electron ics, 11(7), 1111.

10. Amin, R., Al Ghamdi, M. A., Almotiri, S. H., &Alruily, M. (2021). Healthcare techniques through deep learning: issues, challenges and opportunities. IEEE Access, 9, 98523-98541.

11. Soh, D. C. K., Ng, E. Y. K., Jahmunah, V., Oh, S. L., San Tan, R., & Acharya, U. R. (2020). Automated diagnostic tool for hy pertension using convolutional neural network. Computers in Biology and Medicine, 126, 103999.

12. Zhao, M., & Feng, Z. (2020). Machine learning methods to evaluate the depression status of Chinese recruits: a diagnostic study. Neuropsychiatric disease and treatment, 2743-2752.

13. Ahmed, A., Aziz, S., Toro, C. T., Alzubaidi, M., Irshaidat, S., Serhan, H. A., ... &Househ, M. (2022). Machine Learning Models to Detect Anxiety and Depression through Social Me dia: A Scoping Review. Computer Methods and Programs in Biomedicine Update, 100066.

14. AlSagri, H. S., &Ykhlef, M. (2020). Machine learning-based ap proach for depression detection in twitter using content and activity features. IEICE Transactions on Information and Sys tems, 103(8), 1825-1832.

15. Tavchioski, I., Škrlj, B., Pollak, S., & Koloski, B. (2022). Early detection of depression with linear models using hand-crafted and contextual features. Working Notes of CLEF, 5-8.

16. Rab, S., Wan, M., & Yadav, S. (2023). International and National Metrology. https://doi.org/10.1007/978-981-19-1550-5_2-1

Published

2025-03-05

How to Cite

AI-Driven Detection of Depressive Disorders: A Machine Learning Approach . (2025). Trinity Journal of Management, IT & Media (TJMITM), 15(1), 52-59. https://doi.org/10.48165/tjmitm.2024.15.09