title: Aid decision algorithms to estimate the risk in congenital heart surgery.
abstract: OBJECTIVE
  In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death.
  
  
  METHODS
  We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity.
  
  
  RESULTS
  Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio.
  
  
  CONCLUSIONS
  According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.
authors.affiliation: Department of Computer Technology of the University of Alicante, Spain. Electronic address: druiz@dtic.ua.es.
authors.forename: Daniel
authors.initials: D
authors.lastname: Ruiz-Fernández
authors.affiliation: Bio-inspired Engineering and Health Computing Research Group, IBIS, University of Alicante, Spain.
authors.forename: Ana
authors.initials: A
authors.lastname: Monsalve Torra
authors.affiliation: Department of Computer Technology of the University of Alicante, Spain.
authors.forename: Antonio
authors.initials: A
authors.lastname: Soriano-Payá
authors.affiliation: Bio-inspired Engineering and Health Computing Research Group, IBIS, University of Alicante, Spain.
authors.forename: Oscar
authors.initials: O
authors.lastname: Marín-Alonso
authors.affiliation: Paediatric Cardiovascular Surgery Department of Cardiovascular Foundation of Colombia, Colombia.
authors.forename: Eddy
authors.initials: E
authors.lastname: Triana Palencia
mesh_terms: D000465:Algorithms; D006348:Cardiac Surgical Procedures; D002309:Cardiology / Q000379:methods; D000066491:Clinical Decision-Making; D003661:Decision Support Techniques; D003663:Decision Trees; D005260:Female; D006330:Heart Defects, Congenital / Q000601:surgery*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016013:Likelihood Functions; D000069550:Machine Learning; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012306:Risk
keywords: Artificial neural networks; Classifiers; Congenital heart disease; Data analysis; Decision trees