Published on in Vol 8, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29838, first published .
Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

Authors of this article:

Kiran Saqib1 Author Orcid Image ;   Amber Fozia Khan1 Author Orcid Image ;   Zahid Ahmad Butt1 Author Orcid Image

Journals

  1. Bilal A, Fransson E, Bränn E, Eriksson A, Zhong M, Gidén K, Elofsson U, Axfors C, Skalkidou A, Papadopoulos F. Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol. BMJ Open 2022;12(4):e059033 View
  2. Zhang H, Yang G, Dong A, Deng X. Prediction Model between Serum Vitamin D and Neurological Deficit in Cerebral Infarction Patients Based on Machine Learning. Computational and Mathematical Methods in Medicine 2022;2022:1 View
  3. Bartal A, Jagodnik K, Chan S, Babu M, Dekel S. Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives. American Journal of Obstetrics & Gynecology MFM 2023;5(3):100834 View
  4. Liu H, Dai A, Zhou Z, Xu X, Gao K, Li Q, Xu S, Feng Y, Chen C, Ge C, Lu Y, Zou J, Wang S. An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches. Journal of Affective Disorders 2023;328:163 View
  5. Liu H, Shi R, Liao R, Liu Y, Che J, Bai Z, Cheng N, Ma H. Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls. Brain Sciences 2022;12(12):1677 View
  6. Ndaba S. A Review of the Use of R Programming for data Science Research in Botswana. International Journal of Database Management Systems 2023;15(1):1 View
  7. Chen L, Hua J, He X. Genetic analysis of cuproptosis subtypes and immunological features in severe influenza. Microbial Pathogenesis 2023;180:106162 View
  8. Wang M, Richmond L, Schleider J, Nelson B, Luhmann C. Predicting internalizing symptoms with machine learning: identifying individuals that need care. Journal of American College Health 2025;73(5):2248 View
  9. Hong L, Yang A, Liang Q, He Y, Wang Y, Tao S, Chen L. Wife-Mother Role Conflict at the Critical Child-Rearing Stage: A Machine-Learning Approach to Identify What and How Matters in Maternal Depression Symptoms in China. Prevention Science 2024;25(4):699 View
  10. Chen L, Hua J, He X. Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children. BMC Genomics 2023;24(1) View
  11. Arora M, Singh J, Singh A. Development of intelligent system based on synthesis of affective signals and deep neural networks to foster mental health of the Indian virtual community. Social Network Analysis and Mining 2024;14(1) View
  12. Turchioe M, Hermann A, Benda N. Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry. Frontiers in Psychiatry 2024;14 View
  13. Lilhore U, Dalal S, Varshney N, Sharma Y, Rao K, Rao V, Alroobaea R, Simaiya S, Margala M, Chakrabarti P. Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model. Scientific Reports 2024;14(1) View
  14. Lin S, Wang C, Jiang X, Zhang Q, Luo D, Li J, Li J, Xu J. Using machine learning to develop a five-item short form of the children’s depression inventory. BMC Public Health 2024;24(1) View
  15. Naidu P, Ruchitha M, Yaswanth P, Harika B, Prabhu P, Deepthi Sree G. Mental Health Detection using Machine Learning. International Journal of Innovative Science and Research Technology (IJISRT) 2024:760 View
  16. Sadjadpour F, Hosseinichimeh N, Abedi V, Soghier L. Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU. Frontiers in Public Health 2024;12 View
  17. Guo J, Zhao J, Han P, Wu Y, Zheng K, Huang C, Wang Y, Chen C, Guo Q. Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research. Frontiers in Psychiatry 2024;15 View
  18. Göçmez S, Gür E. Postpartum komplikasyon yönetiminde yapay zekâ teknolojisi ve ebelik bakımına katkısı. Anatolian Journal of Health Research 2024;5(2):189 View
  19. Nasim S, Sami Al-Shamayleh A, Thalji N, Raza A, Abualigah L, Ibrahim Alzahrani A, Alwadain A, Mohammed Alsekait D, Migdady H, Salama Abd Elminaam D. Novel Meta Learning Approach for Detecting Postpartum Depression Disorder Using Questionnaire Data. IEEE Access 2024;12:101247 View
  20. Shah P, Yadav K, Prabhakar P. From Detection to Recovery: The Promise of AI in Managing Postpartum Depression in India. E3S Web of Conferences 2024;556:01047 View
  21. Mapari S, Shrivastava D, Dave A, Bedi G, Gupta A, Sachani P, Kasat P, Pradeep U. Revolutionizing Maternal Health: The Role of Artificial Intelligence in Enhancing Care and Accessibility. Cureus 2024 View
  22. Shivaprasad S, Chadaga K, Sampathila N, Prabhu S, Chadaga P R, K S S. Explainable machine learning methods to predict postpartum depression risk. Systems Science & Control Engineering 2024;12(1) View
  23. Tay J, Ang Y, Tam W, Sim K. Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review. BMJ Open 2025;15(2):e084463 View
  24. Lu E, Zhang D, Han M, Wang S, He L. The application of artificial intelligence in insomnia, anxiety, and depression: A bibliometric analysis. DIGITAL HEALTH 2025;11 View
  25. Wei S, Guo X, He S, Zhang C, Chen Z, Chen J, Huang Y, Zhang F, Liu Q. Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e67871 View
  26. Huang X, Zhang L, Zhang C, Li J, Li C. Postpartum depression in Northeastern China: a cross-sectional study 6 weeks after giving birth. Frontiers in Public Health 2025;13 View
  27. Lorenzoni G, Tavares C, Nascimento N, Alencar P, Cowan D, Mehmood A. Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study. PLOS One 2025;20(5):e0322299 View
  28. Xie Y, Zheng H, Gan W, Su C, Shams M, Yang J. The performance of machine learning models in predicting postpartum depression: a meta-analysis and systematic review. Journal of Reproductive and Infant Psychology 2025;43(5):1093 View
  29. Zhu X, Chen Y, Jiang Z, Bi R, Zhang Q, Chen Y, Jiang Y, Cao Y, Dong G. Assessment of suicidal risk factors in young depressed persons with non-suicidal self-injury based on an artificial intelligence. BMC Psychology 2025;13(1) View
  30. Huang X, Zhang L, Zhang C, Li J, Li C. Postpartum depression risk prediction using explainable machine learning algorithms. Frontiers in Medicine 2025;12 View
  31. Xia J, Chen C, Lu X, Zhang T, Wang T, Wang Q, Zhou Q. Artificial intelligence-oriented predictive model for the risk of postpartum depression: a systematic review. Frontiers in Public Health 2025;13 View
  32. Oǧur N, Çeken C, Selím Oǧur Y, Yazici E. A Scalable Framework for Big Data Analytics in Psychological Research: Leveraging Distributed Systems and Cluster Management. IEEE Access 2025;13:174947 View
  33. Huang L, Yang W, Wang L, Wang P, Lin Y, Yao Y, Zhao Z, Zhang F, Zhang H, Liao L, Hu J, Ye Y, Yuan J, Liu Y. Identifying vulnerable groups of community-dwelling older adults with a strong willingness to receive volunteer services based on machine learning methods. BMC Public Health 2025;25(1) View

Books/Policy Documents

  1. Warsi M, Gupta S, Dev R, Kundu K, Singh P. Intelligent IT Solutions to Promote Indigenous Innovations. View
  2. Rajeesh F, Haque W. Bioinformatics and Computational Biology. View

Conference Proceedings

  1. Ndaba S. Artificial Intelligence, Soft Computing and Applications. Review of the use of R Programming for Data Analysis in Botswana Research View
  2. Chahar R, Dubey A, Narang S. 2023 3rd International Conference on Intelligent Technologies (CONIT). A Mental Health Performance Assessment using Support Vector Machine View
  3. Marshad I, Islam M, Samin A, Shomyo M, Nishat M, Faisal F. 2024 International Conference on Inventive Computation Technologies (ICICT). Optimizing Maternal Mental Health: A Study on Boosting Algorithms for Suicidal Tendencies Prediction in Postpartum Depression View
  4. Anand , Sharma Y, Jain V, Tarwani S. 2024 IEEE Region 10 Symposium (TENSYMP). Ensemble Machine Learning Model for Predicting Postpartum Depression Disorder View
  5. Hosaain M, Kashem M, Nayan N. 2024 9th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM). Artificial Intelligence-Driven Approach for Predicting Maternal Health Risk Factors View
  6. Rameshkumar P, Mohanraj J, Elango K, Palanisamy M. INTERNATIONAL CONFERENCE ON GREEN COMPUTING FOR COMMUNICATION TECHNOLOGIES (ICGCCT – 2024). Machine learning approaches forpredicting postpartum depression risk leveraging XGBoost and CatBoost algorithms View
  7. Dust A, Levitt P, Matarić M. 2024 12th International Conference on Affective Computing and Intelligent Interaction (ACII). Behind the Smile: Mental Health Implications of Mother-Infant Interactions Revealed Through Smile Analysis View
  8. S M, R K, P D, D N. 2024 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). Bridging Technology and Healthcare: Machine Learning in Postpartum Depression Risk Assessment and a Proposed MMSM (Maternal Mind Support Module) View
  9. Zaman Z, Nova T, Riya L, Sarker T, Easha I, Akter E, Khan F. 2024 27th International Conference on Computer and Information Technology (ICCIT). Machine Learning Based Depression Prediction: Comparative Analysis of Models and Stacked Ensemble Approach View
  10. S S, C P S. 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA). From Data to Diagnosis: A Review of Machine Learning Models for Postpartum Depression Prediction View
  11. Green D, Shang Y, Cheong J, Liu Y, Gunes H. 2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition (FG). Gender Fairness of Machine Learning Algorithms for Pain Detection View