Identification and Validation of a Novel Machine Learning Model for Predicting Severe Pelvic Endometriosis: A Retrospective Study

Siqi Cao, Xingzhe Li, Xin Zheng, Jiaxin Zhang, Ziyao Ji, yanjun liu, Yiliang Liu
In: Research Square · 2024 · doi:10.21203/rs.3.rs-5309546/v1 · W4405464258
preprint OA: green CC0
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⚙ AI-generated summary by claude@2026-06+body, 2026-06-09 ⓘ

This retrospective study identified factors for severe endometriosis and developed a random forest machine learning model, validated by SHAP analysis, that accurately predicts severe disease.

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Outcome instruments

rASRM Enzian

Condition tags

endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (19)

Source provenance

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