Aid decision algorithms to estimate the risk in congenital heart surgery

Aid decision algorithms to estimate the risk in congenital heart surgery

Highlights • We propose an alternative system for classifying the risk in paediatric congenital heart surgery. • Four methods are tested: a perceptron multilayer, self-organising maps, a radial basis function neural network and decision trees. • We obtain an accuracy of 99.87% (using pre and post-su...

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Veröffentlicht in:Computer methods and programs in biomedicine 2016-04, Vol.126, p.118-127
Hauptverfasser: Ruiz-Fernández, Daniel, Monsalve Torra, Ana, Soriano-Payá, Antonio, Marín-Alonso, Oscar, Triana Palencia, Eddy
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Artificial neural networks
Cardiac Surgical Procedures
Cardiology - methods
Classification
Classifiers
Clinical Decision-Making
Complexity
Congenital heart disease
Data analysis
Decision Support Techniques
Decision Trees
Female
Heart Defects, Congenital - surgery
Heart diseases
Humans
Infant
Infant, Newborn
Internal Medicine
Likelihood Functions
Machine Learning
Male
Models, Statistical
Multilayer perceptrons
Neural Networks (Computer)
Other
Reproducibility of Results
Risk
Surgery
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Zusammenfassung:Highlights • We propose an alternative system for classifying the risk in paediatric congenital heart surgery. • Four methods are tested: a perceptron multilayer, self-organising maps, a radial basis function neural network and decision trees. • We obtain an accuracy of 99.87% (using pre and post-surgical data) and 83% (using just pre-surgical data).
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2015.12.021