Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.

ABSTRACT:

Aims

To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients.

Method

This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and precision.

Results

In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0

SUBMITTER: Song YX 

PROVIDER: S-EPMC9804041 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature