Construction and Validation of a Model for Predicting Cervical Intraepithelial Neoplasia Grade II+: A Cross-Sectional Population Study via Machine Learning.

Background: Cervical cancer, as the leading malignant tumor among women globally, underscores the critical need for early screening; however, effective models for predicting cervical lesions remain lacking. Objective: To construct a predictive model for cervical intraepithelial neoplasia II+(CINII+), and to compare the predictive performance of machine learning models integrating thinprep cytologic test (TCT) + human papillomavirus (HPV) testing with clinical data versus TCT combined with traditional clinical data for CIN II+. Methods: Clinical data from women undergoing cervical cancer screening at Linquan Maternity and Child Healthcare Hospital (2020-2024) were collected, including TCT results, HPV status, cervical pathology, age, sexual history and other clinical data. Ten machine learning algorithms were applied to develop two predictive models: Model 1(TCT+HPV+clinical data) and Model 2(TCT+traditional clinical data). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA). Results: Multivariate logistic regression analysis showed that HPV positivity, TCT indicates High-Grade Squamous Intraepithelial Lesion(HSIL), colposcopy result indicates a high-grade lesion and the first age of pregnancy as predictors of CINII+. Model 1 (TCT+HPV+clinical data) demonstrated significantly higher predictive efficacy than Model 2(TCT+clinical data), the difference in AUC is statistically significant. (P=0.006 in training set; P=0.035 in testing set). Conclusion: The TCT+HPV-integrated model outperformed the TCT-only model in predicting CIN II+, supporting the incorporation of HPV testing into routine screening to enhance early diagnostic accuracy.