Predicting Mortality in Patients with Chronic Heart Failure

Chronic heart failure affects many people worldwide. A random forest model was created in this study to help predict if the patients die or survive. The model was based on the data from MUSIC database and on data extracted from highresolution ECG records in the database. In total, 40 features were used for the training. The model achieved 0.50 F1 score, 0.73 AUROC and 0.60 AUPRC on test set. The most significant features were ”Pro BNP (ng/L)” and ”Urea (mg/dL)”.

Publication Details

Journal
Brno University of Technology Digital Library (Brno University of Technology)
Published
2025-01-01
Primary Topic
Nuclear Structure and Function
Type
article
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article

Predicting Mortality in Patients with Chronic Heart Failure

Brno University of Technology Digital Library (Brno University of Technology)
Nuclear Structure and Function
article

Predicting Mortality in Patients with Chronic Heart Failure

article en

Abstract

Chronic heart failure affects many people worldwide. A random forest model was created in this study to help predict if the patients die or survive. The model was based on the data from MUSIC database and on data extracted from highresolution ECG records in the database. In total, 40 features were used for the training. The model achieved 0.50 F1 score, 0.73 AUROC and 0.60 AUPRC on test set. The most significant features were ”Pro BNP (ng/L)” and ”Urea (mg/dL)”.

Brno University of Technology Digital Library (Brno University of Technology)
Good health and well-being
Openalex Percentile: Top 51%
Nuclear Structure and Function
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Predicting Mortality in Patients with Chronic Heart Failure · Brno University of Technology Digital Library (Brno University of Technology) (2025) | TGRS Research Map | TGRS