{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:51:45Z","timestamp":1782989505035,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686684","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,29]]},"abstract":"<jats:p>Postpartum depression (PPD) affects nearly one in five mothers, yet many cases remain undetected. This study demonstrates the value of a leakage-resistant, rigorously designed machine learning pipeline for early prediction using psychosocial data from 1,430 women during pregnancy. Concept-level feature harmonization addressed data artifacts, while repeated group-aware cross-validation, calibration, and recall-oriented thresholding ensured robust evaluation. Class imbalance was handled using weighting and SMOTE. Models showed strong performance (ROC-AUC: 0.762\u20130.801; PR-AUC: 0.400\u20130.490), with logistic regression and ensemble methods performing best. High recall (&gt;0.90) supports screening use, though moderate precision highlights the need for clinical follow-up. SHAP analysis identified anxious attachment and coping factors as key predictors. Overall, well-designed ML pipelines can enable early, reliable PPD risk stratification while minimizing bias and leakage.<\/jats:p>","DOI":"10.3233\/shti260948","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:03:59Z","timestamp":1782986639000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting Postpartum Depression Using Imbalance-Aware Machine Learning"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9384-7053","authenticated-orcid":false,"given":"Aliya","family":"Tabassum","sequence":"first","affiliation":[{"name":"Qatar University, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7644-8604","authenticated-orcid":false,"given":"Mais","family":"Alkhateeb","sequence":"additional","affiliation":[{"name":"Lusail University, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2439-3267","authenticated-orcid":false,"given":"Almuthana","family":"Alhussain","sequence":"additional","affiliation":[{"name":"King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4025-5767","authenticated-orcid":false,"given":"Arfan","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine-Qatar, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7695-4626","authenticated-orcid":false,"given":"Alaa","family":"Abd-Alrazaq","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine-Qatar, Doha, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Health Sciences Informatics Leads and Empowers the Digital Health Transformation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260948","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:03:59Z","timestamp":1782986639000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260948"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"ISBN":["9781643686684"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260948","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]}}}