Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost.

ABSTRACT:

Background

Early prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the eXtreme Gradient Boosting (XGBoost) algorithm to prognosticate CCU patients and compared XGBoost with traditional classification models.

Methods

CCU patients' data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: <30 days, 30 days-1 year, 1-5 years, and ≥5 years. Four classification models, including XGBoost, naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM) were constructed using the Python software. These four models were tested and compared for accuracy, F1 score, Matthews correlation coefficient (MCC), and area u

SUBMITTER: Wang X 

PROVIDER: S-EPMC9133425 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature