Interpretable XGBoost model identifies idiopathic central precocious puberty in girls using four clinical and imaging features

Interpretable XGBoost model identifies idiopathic central precocious puberty in girls using four clinical and imaging features

Abstract Background The study aimed to develop interpretable machine learning models for the identification of idiopathic central precocious puberty (ICPP) in girls, without the need for the expensive and time-consuming gonadotropin-releasing hormone (GnRH) stimulation test, which is currently the g...

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Bibliographic Details
Main Authors: Lu Tian, Yan Zeng, Helin Zheng, Jinhua Cai
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Endocrine Disorders
Subjects:
Machine learning
Interpretable
Idiopathic central precocious puberty
Girl
Online Access:https://doi.org/10.1186/s12902-025-01983-4
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