Machine Learning in Early Screening for High-Grade Cervical Intraepithelial Neoplasia Using Blood Testing

Background: High-grade cervical intraepithelial neoplasia (CIN2/3) is a critical precursor to cervical cancer, yet current screening methods (e.g., HPV testing, colposcopy) face challenges in accessibility and invasiveness, especially in resource-limited settings. We aimed to develop a non-invasive, machine learning (ML)-based model using routine blood biomarkers to predict high-grade CIN, offering a scalable and cost-effective screening alternative. Methods: Data from 128 cases of high-grade CIN and 120 cases of low-grade CIN were collected from a hospital in China. A total of 29 clinical characteristics and blood test measurements were considered for use in model development. Four feature selection algorithms (F-test, LASSO regression, decision tree, and random forest) were used to identify key predictors, and 11 machine learning algorithms were employed for model training. The dataset was split into training (70%) and testing (30%) cohorts. Model performance was evaluated using learning curves, receiver operating characteristic curves (ROC), area under the curve (AUC), Brier score, calibration curves, Precision-Recall (PR) curves, and Decision Curve Analysis (DCA). A web-based calculator was developed for clinical deployment. Results: Key features selected for the model included creatinine (CREA), red blood cell count (RBC), neutrophil percentage (NEU%), direct bilirubin (DBIL), and monocyte count (MON). The Support Vector Machine (SVM) algorithm achieved the best predictive performance, with an AUC of 0.75 and a Brier score of 0.21. The web tool (https://dvhl6xsf29zmdewixjx7kz.streamlit.app) provides real-time risk stratification. Conclusions: The model demonstrated strong performance across various validation metrics, indicating potential clinical utility. We also developed a web-based calculator to estimate high-grade CIN.