Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery

Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery

<b>Background:</b> The objective of this IRB-approved retrospective monocentric study was to identify risk factors for mortality after surgery for congenital heart defects (CHDs) in pediatric patients using machine learning (ML). CHD belongs to the most common congenital malformations, a...

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Bibliographic Details
Main Authors: Lorenz A. Kapsner, Manuel Feißt, Ariawan Purbojo, Hans-Ulrich Prokosch, Thomas Ganslandt, Sven Dittrich, Jonathan M. Mang, Wolfgang Wällisch
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Diagnostics
Subjects:
risk factors
mortality
congenital heart defects (CHDs)
machine learning (ML)
random survival forest (RSF)
eXtreme Gradient Boosting (XGB)
Online Access:https://www.mdpi.com/2075-4418/14/22/2587
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https://www.mdpi.com/2075-4418/14/22/2587

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