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

Hong Tao1, Zhengjiao Tong2, Hongtao Li1

  • 1Department of Medicine Statistics, Hunan Hoomya Gene Technology Co., Ltd., Changsha, 410205, China.

BMC Medical Informatics and Decision Making
|February 25, 2026
PubMed
Abstract

No abstract available in PubMed .

Keywords:
Cervical cancerCervical intraepithelial neoplasiaCytologyHPVMachine learningXGBoost

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