Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning.

ABSTRACT:

Background

Short-term unplanned readmission is always neglected, especially for elderly patients with coronary heart disease (CHD). However, tools to predict unplanned readmission are lacking. This study aimed to establish the most effective predictive model for the unplanned 7-day readmission in elderly CHD patients using machine learning (ML) algorithms.

Methods

The detailed clinical data of elderly CHD patients were collected retrospectively. Five ML algorithms, including extreme gradient boosting (XGB), random forest, multilayer perceptron, categorical boosting, and logistic regression, were used to establish predictive models. We used the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, the F1 value, the Brier score, the area un

SUBMITTER: Song X 

PROVIDER: S-EPMC10442485 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature