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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | BMC Endocrine Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12902-025-01983-4 |
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