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

BACKGROUND: Effective risk stratification is crucial for managing cervical lesions. This study aimed to develop and evaluate machine learning models to improve the detection of cervical intraepithelial neoplasia (CIN) grade 2 or worse (CIN2+) and CIN3+. METHODS: This retrospective study included 2,863 participants. We developed three models: a Logistic Regression (Logit.Model), a Random Forest (RF.Model), and an XGBoost (XGBoost.Model) to predict CIN2 + and CIN3 + status. The dataset was split into training (60%) and test (40%) sets. Model performance was assessed using AUC, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The SHapley Additive explanation (SHAP) method was employed for model interpretation. RESULTS: XGBoost.Model demonstrated robust performance, achieving the highest test set AUCs of 0.735 for CIN2 + and 0.841 for CIN3+. It showed significantly higher AUC for CIN3 + detection compared to RF.Model (P < 0.001). XGBoost.Model also provided significant NRI (13.1% for CIN2+, 28.0% for CIN3+) and IDI (12.1% for CIN2+, 11.0% for CIN3+) over the Logit.Model (all P < 0.05). SHAP analysis confirmed the model’s interpretability, highlighting key predictive features such as cytology and specific HPV genotypes. CONCLUSIONS: The XGBoost.Model exhibited superior and consistent performance, achieving the highest test set AUC and providing a significant NRI and IDI over the logistic regression model. CLINICAL TRIAL NUMBER: Not applicable.