Comparison of Unsupervised Machine Learning Approaches for Cluster Analysis to Define Subgroups of Heart Failure with Preserved Ejection Fraction with Different Outcomes

Comparison of Unsupervised Machine Learning Approaches for Cluster Analysis to Define Subgroups of Heart Failure with Preserved Ejection Fraction with Different Outcomes

Heart failure with preserved ejection (HFpEF) is a heterogenous condition affecting nearly half of all patients with heart failure (HF). Artificial intelligence methodologies can be useful to identify patient subclassifications with important clinical implications. We sought a comparison of differen...

Full description

Bibliographic Details
Main Authors: Hirmand Nouraei, Hooman Nouraei, Simon W. Rabkin
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Bioengineering
Subjects:
heart failure
preserved ejection fraction
unsupervised machine learning
cluster analysis
Online Access:https://www.mdpi.com/2306-5354/9/4/175

Internet

https://www.mdpi.com/2306-5354/9/4/175

Similar Items