Systematic review identifies machine learning models for predicting preeclampsia and gestational diabetes using clinical data and paternal factors, highlighting implications for maternal health.
Systematic literature review (PROSPERO CRD420251061201) of machine-learning prediction models for preeclampsia (PE) and gestational diabetes (GDM) using routinely collected clinical data, with paternal and partner variables as a primary analytic axis. 446 studies were screened across five databases (PubMed/MEDLINE, Scopus, Web of Science, Embase, IEEE Xplore; January 2021 to March 2026) and appraised with a dual-reviewer eight-criterion checklist (C1 to C8, threshold 4.5/8.0). Key findings: only 2 of 446 models (0.4 percent) used a paternal variable as a model input; the random-effects pooled AUC-ROC was 0.836 (95 percent CI 0.760 to 0.892); external validation was reported by 25.3 percent of studies and calibration by 12.3 percent. This deposit contains the manuscript, the LaTeX sources, and the full reproducibility package (extraction dataset schema, analysis code, and protocol) enabling reproduction of every reported quantity.
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