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. 2026 Jun 4;13:1808657. doi: 10.3389/fmed.2026.1808657

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Copyright © 2026 Zhu, Zeng, Lan, Liang, Cai, Gu and Guo.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Flowchart diagram showing a cohort of 189 patients with thrombosis and non-thrombosis cases, feature selection using various machine learning models where Random Forest achieved best performance, and SHAP analysis explaining model predictions for training and validation sets with key predictive factors listed. — Machine learning pipeline. The figure graphically summarizes the machine learning steps.