Random forest algorithm for predicting postoperative delirium in older patients.

ABSTRACT:

Objective

In this study, we were aimed to identify important variables via machine learning algorithms and predict postoperative delirium (POD) occurrence in older patients.

Methods

This study was to make the secondary analysis of data from a randomized controlled trial. The Boruta function was used to screen relevant basic characteristic variables. Four models including Logistic Regression (LR), K-Nearest Neighbor (KNN), the Classification and Regression Tree (CART), and Random Forest (RF) were established from the data set using repeated cross validation, hyper-parameter optimization, and Smote technique (Synthetic minority over-sampling technique, Smote), with the calculation of confusion matrix parameters and the plotting of Receiver operating characteristic curve (ROC),

SUBMITTER: Sheng W 

PROVIDER: S-EPMC10808713 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature