Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model.

ABSTRACT: Heart failure is a devastating disease that has high mortality rates and a negative impact on quality of life. Heart failure patients often experience emergency readmission after an initial episode, often due to inadequate management. A timely diagnosis and treatment of underlying issues can significantly reduce the risk of emergency readmissions. The purpose of this project was to predict emergency readmissions of discharged heart failure patients using classical machine learning (ML) models based on Electronic Health Record (EHR) data. The dataset used for this study consisted of 166 clinical biomarkers from 2008 patient records. Three feature selection techniques were studied along with 13 classical ML models using five-fold cross-validation. A stacking ML model was trained using the pr

SUBMITTER: Rahman MS 

PROVIDER: S-EPMC10252957 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature