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Additional file 1 of A machine learning-based framework for predicting ovarian response and OHSS risk in GnRH antagonist IVF/ICSI cycles: a multi-objective optimization approach

Year: 2026

Type: other

Abstract: Supplementary Material 1: SHAP feature importance for the OHSS prediction model. Feature importance is ranked by mean absolute SHAP value for the moderate-to-severe OHSS prediction model. AMH and starting dose were identified as the primary risk drivers, with higher values associated with increased probability of moderate-to-severe OHSS. AFC and age demonstrated moderate contributions, whereas infertility-related factors showed relatively lower importance.

Source: Figshare

DOI: 10.6084/m9.figshare.33044999

Authors: Junbiao Mao, Shuhong Luo, Junling Wang, Ben Yuan

Institutions: Hubei Polytechnic University, Wuhan University of Science and Technology

Language: English

Topic: Ovarian function and disorders

Subfield: Reproductive Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7169877030

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