The predictive value of neural network models and random forest models for the classification of cervical intraepithelial lesions based on gene methylation and HPV infection genotype

Abstract Objective This study aims to evaluate the application value of neural network (NN) and random forest (RF) models integrating gene methylation markers and HPV infection typing data in the prediction of cervical intraepithelial neoplasia (CIN) grading, providing new tools for the precise screening of clinical cervical cancer. Methods Clinical data of 138 patients with cervical lesions who were treated from September 2024 to September 2025 were retrospectively collected. Among them, there were 36 cases (26.1%) in the control group, 63 cases (45.6%) with low - grade squamous intraepithelial lesion (LSIL), and 39 cases (28.3%) with high - grade squamous intraepithelial lesion (HSIL). (ASTN1, DLX1, ITGA4, RXFP3, SOX17, ZNF671) were detected. The NN and RF models were constructed. The AUC, sensitivity, specificity, and accuracy of the two models in different types of cervical intraepithelial lesions were compared and verified. Results There were significant differences in general clinical data, laboratory indicators, and pathological indicators among the three groups of patients: no intraepithelial neoplasia (NILM) in the control group, LSIL, and HSIL. There were statistically significant differences in CD4 + T, IL − 2, HPV infection typing, and gene methylation among the three groups of patients (P < 0.05). In the validation set, the sensitivity, specificity, accuracy, and AUC of the random forest model in predicting different types of cervical intraepithelial lesions were higher than those of the neural network model. Conclusion The random forest model integrating gene methylation and HPV infection typing performs best in the prediction of CIN grading, with high precision and clinical interpretability. It can be used as an efficient tool for the triage of HPV - positive populations and contribute to the optimization of cervical cancer screening strategies.