Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure.

ABSTRACT:

Aims

Predicting the risk of malignant arrhythmias (MA) in hospitalized patients with heart failure (HF) is challenging. Machine learning (ML) can handle a large volume of complex data more effectively than traditional statistical methods. This study explored the feasibility of ML methods for predicting the risk of MA in hospitalized HF patients.

Methods and results

We evaluated the baseline data and MA events of 2794 hospitalized HF patients in the HF cohort in Anhui Province and randomly divided the study population into training and validation sets in a 7:3 ratio. The Lasso-logistic regression, multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost) algorithms were used to const

SUBMITTER: Wang Q 

PROVIDER: S-EPMC8712774 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature