XGBoost algorithm and logistic regression to predict the postoperative 5-year outcome in patients with glioma.

ABSTRACT:

Background

Glioma is the most common primary intracranial tumor with poor prognosis. The prediction of glioma prognosis has not been well investigated. XGBoost algorithm has been widely used in and data analysis. The predictive value of XGBoost algorithm in glioma remains unclear. This current study used the XGBoost algorithm to construct a predictive model for postoperative outcomes of glioma patients.

Methods

Patients with glioma who underwent surgery from January 2006 to April 2017 were retrospectively included in this study. Clinical and follow-up data were collected. The XGBoost model and multivariate logistic regression analysis model were used to screen the factors related to postoperative outcomes, and the results of the two models were compared. The area under the r

SUBMITTER: Yan Z 

PROVIDER: S-EPMC9469144 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature