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Original Article
Computational Intelligence for Postpartum Depression Prediction: A Comparative Deep Learning Study
Tamil Elakya T1
Dr. K Manikandan2
1 Research Scholar, Department of Computer Science, PSG College of Arts & Science, Coimbatore, Tamilnadu, India. 2 Head & Associate Professor, Department of Computer Science, PSG College of Arts & Science, Coimbatore, Tamilnadu, India.
Published Online: May-August 2026
Pages: 828-831
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502088
References
1. Beck, C. T.. (2001). Predictors of postpartum depression: An update. Nursing Research, 50(5), 275–285.
https://doi.org/10.1097/00006199-200109000-00004
2. Cox, J. L., Holden, J. M., & Sagovsky, R.. (1987). Detection of postnatal depression: Development of the Edinburgh Postnatal Depression
Scale. British Journal of Psychiatry, 150(6), 782–786.
3. World Health Organization. (2022). World mental health report: Transforming mental health for all. Geneva, Switzerland.
4. American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.).
5. Goodfellow, I., Bengio, Y., & Courville, A.. (2016). Deep learning. MIT Press.
6. LeCun, Y., Bengio, Y., & Hinton, G.. (2015). Deep learning. Nature, 521(7553), 436–444.
7. Kingma, D. P., & Ba, J.. (2015). Adam: A method for stochastic optimization. International Conference on Learning Representations.
8. Rumelhart, D. E., Hinton, G. E., & Williams, R. J.. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–
536.
9. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R.. (2014). Dropout: A simple way to prevent neural networks
from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
10. Hochreiter, S., & Schmidhuber, J.. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
11. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
12. Dennis, C. L., & Dowswell, T.. (2013). Psychosocial and psychological interventions for preventing postpartum depression. Cochrane
Database of Systematic Reviews.
13. O'Hara, M. W., & McCabe, J. E.. (2013). postpartum depression: Current status and future directions. Annual Review of Clinical
Psychology, 9, 379–407.
14. Stewart, D. E., & Vigod, S.. (2016). Postpartum depression. New England Journal of Medicine, 375(22), 2177–2186.
15. UNICEF. (2021). The state of the world's children 2021: On my mind—Promoting, protecting and caring for children's mental health.
16. Topol, E. J.. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
17. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
18. Rajpurkar, P., et al. (2022). AI in health and medicine. Nature Medicine, 28, 31–38.
19. Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
20. TensorFlow. (2023). TensorFlow: An end-to-end open-source machine learning platform. Google.
21. Keras. (2023). Keras: Deep learning for humans. Google.
22. Howard, J., & Gugger, S.. (2020). Deep learning for coders with fastai and PyTorch. O'Reilly Media.
https://doi.org/10.1097/00006199-200109000-00004
2. Cox, J. L., Holden, J. M., & Sagovsky, R.. (1987). Detection of postnatal depression: Development of the Edinburgh Postnatal Depression
Scale. British Journal of Psychiatry, 150(6), 782–786.
3. World Health Organization. (2022). World mental health report: Transforming mental health for all. Geneva, Switzerland.
4. American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.).
5. Goodfellow, I., Bengio, Y., & Courville, A.. (2016). Deep learning. MIT Press.
6. LeCun, Y., Bengio, Y., & Hinton, G.. (2015). Deep learning. Nature, 521(7553), 436–444.
7. Kingma, D. P., & Ba, J.. (2015). Adam: A method for stochastic optimization. International Conference on Learning Representations.
8. Rumelhart, D. E., Hinton, G. E., & Williams, R. J.. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–
536.
9. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R.. (2014). Dropout: A simple way to prevent neural networks
from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
10. Hochreiter, S., & Schmidhuber, J.. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
11. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
12. Dennis, C. L., & Dowswell, T.. (2013). Psychosocial and psychological interventions for preventing postpartum depression. Cochrane
Database of Systematic Reviews.
13. O'Hara, M. W., & McCabe, J. E.. (2013). postpartum depression: Current status and future directions. Annual Review of Clinical
Psychology, 9, 379–407.
14. Stewart, D. E., & Vigod, S.. (2016). Postpartum depression. New England Journal of Medicine, 375(22), 2177–2186.
15. UNICEF. (2021). The state of the world's children 2021: On my mind—Promoting, protecting and caring for children's mental health.
16. Topol, E. J.. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
17. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
18. Rajpurkar, P., et al. (2022). AI in health and medicine. Nature Medicine, 28, 31–38.
19. Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
20. TensorFlow. (2023). TensorFlow: An end-to-end open-source machine learning platform. Google.
21. Keras. (2023). Keras: Deep learning for humans. Google.
22. Howard, J., & Gugger, S.. (2020). Deep learning for coders with fastai and PyTorch. O'Reilly Media.
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