Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.

ABSTRACT: BACKGROUND Cardiac arrest (CA) is a global public health challenge. This study explored the predictors of mortality and their interactions utilizing machine learning algorithms and their related mortality odds among patients following CA. MATERIAL AND METHODS The study retrospectively investigated 161 medical records of CA patients admitted to the Intensive Care Unit (ICU). The random forest classifier algorithm was used to assess the parameters of mortality. The best classification trees were chosen from a set of 100 trees proposed by the algorithm. Conditional mortality odds were investigated with the use of logistic regression models featuring interactions between variables. RESULTS In the logistic regression model, male sex was associated with 5.68-fold higher mortality odds. The morta

SUBMITTER: Lewandowski L 

PROVIDER: S-EPMC11323708 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature