Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia

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Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2026-02-25
DOI
https://doi.org/10.1186/s12911-026-03413-4
Primary Topic
Cervical Cancer and HPV Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia

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BMC Medical Informatics and Decision Making
Cervical Cancer and HPV Research
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Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia

Tao Hong, Saiping Mao, Xing Fan, Hongtao Li, Zhengjiao Tong
article en

Abstract

No abstract available for this paper.
BMC Medical Informatics and Decision Making
Hunan Normal University (CN), Annoroad Gene Technology (China) (CN), Changsha Hospital for Maternal and Child Health Care (CN)
Zero hunger
Openalex Percentile: Top 44%
Cervical Cancer and HPV Research
DOIOpen Access PDFOpenAlex
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