ArticleBMC medical informatics and decision making2026
Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEffective 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+.
methodsThis 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.
resultsXGBoost.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.
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.