Machine Learning Model-Based Prediction of In-Hospital Acute Kidney Injury Risk in Acute Aortic Dissection Patients.

ABSTRACT:

Background

This study aimed to identify the risk factors for in-hospital acute kidney injury (AKI) in patients with acute aortic dissection (AAD) and to establish a machine learning model for predicting in-hospital AKI.

Methods

We extracted data on patients with AAD from the Medical Information Mart for Intensive Care (MIMIC)-IV database and developed seven machine learning models: support vector machine (SVM), gradient boosting machine (GBM), neural network (NNET), eXtreme gradient boosting (XGBoost), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), and the optimal model was interpreted using Shapley Additive expla

SUBMITTER: Wei Z 

PROVIDER: S-EPMC11868902 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature