A multi-variable predictive warning model for cervical cancer using clinical and SNPs data.

ABSTRACT:

Introduction

Cervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).

Methods

Clinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to co

SUBMITTER: Li X 

PROVIDER: S-EPMC10918689 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature