Machine learning-based risk prediction model development for acute kidney injury in type 2 myocardial infarction patients.

ABSTRACT: Type 2 myocardial infarction (T2MI), distinguished from Type 1 myocardial infarction (T1MI) by oxygen supply - demand mismatch, has unique features. Acute kidney injury (AKI) following MI leads to severe consequences. Existing research mostly centers on T1MI, leaving a gap in T2MI related AKI studies. To address this, our research aims to explore AKI risk factors in T2MI patients and leverage machine learning algorithms to develop a model for accurate early prediction of AKI risk in this patient group. This retrospective study utilized the MIMIC-IV database (2008-2022) to analyze T2MI patients in critical care. The dataset was split 70:30 for model development. 12 machine learning algorithms underwent Boruta-based feature selection and hyperparameter optimization. All 12 machine learning a

SUBMITTER: Guo P 

PROVIDER: S-EPMC12770387 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature