The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models.

ABSTRACT:

Objective

Chronic kidney disease (CKD) patients with coronary artery disease (CAD) in the intensive care unit (ICU) have higher in-hospital mortality and poorer prognosis than patients with either single condition. The objective of this study is to develop a novel model that can predict the in-hospital mortality of that kind of patient in the ICU using machine learning methods.

Methods

Data of CKD patients with CAD were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Boruta algorithm was conducted for the feature selection process. Eight machine learning algorithms, such as logistic regression (LR), random forest (RF), Decision Tree, K-nearest neighbors (KNN), Gradient Boosting Decision Tree Machine (GBDT), Support Vector Machine (SVM),

SUBMITTER: Ye Z 

PROVIDER: S-EPMC9847092 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature