A Machine Learning-Based Prediction Model for Acute Kidney Injury in Patients With Congestive Heart Failure.

ABSTRACT:

Background

Machine learning (ML) has been used to build high performance prediction model. Patients with congestive heart failure (CHF) are vulnerable to acute kidney injury (AKI) which makes treatment difficult. We aimed to establish an ML-based prediction model for the early identification of AKI in patients with CHF.

Methods

Patients data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database, and patients with CHF were selected. Comparisons between several common ML classifiers were conducted to select the best prediction model. Recursive feature elimination (RFE) was used to select important prediction features. The model was improved using hyperparameters optimization (HPO). The final model was validated using an external validatio

SUBMITTER: Peng X 

PROVIDER: S-EPMC8931220 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature