Prediction of periventricular leukomalacia.
Part II: Selection of hemodynamic features
using computational intelligence
, Geoffrey L. Bird
, Marijn Kuijpers
Robert A. Zimmerman
, Gail P. Jarvik
, Gil Wernovsky
Robert R. Clancy
, Daniel J. Licht
, J. William Gaynor
Department of Mechanical Engineering, Villanova University, 800 Lancaster Avenue, Villanova,
PA 19085, USA
Division of Critical Care Medicine and Cardiology, Children’s Hospital of Philadelphia,
Philadelphia, PA 19104, USA
Division of Cardiothoracic Surgery, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
Division of Neuroradiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
Department of Medicine (Medical Genetics), University of Washington, Seattle, WA 98195, USA
Academic Medical Center, University of Amsterdam, Department of Anesthesiology,
Received 27 May 2008; received in revised form 8 August 2008; accepted 1 December 2008
Artiﬁcial Intelligence in Medicine (2009) 46, 217—231
Objective: The objective of Part II is to analyze the dataset of extracted hemody-
namic features (Case 3 of Part I) through computational intelligence (CI) techniques
for identiﬁcation of potential prognostic factors for periventricular leukomalacia
(PVL) occurrence in neonates with congenital heart disease.
Methods: The extracted features (Case 3 dataset of Part I) were used as inputs to CI
based classiﬁers, namely, multi-layer perceptron (MLP) and probabilistic neural
network (PNN) in combination with genetic algorithms (GA) for selection of the most
suitable features predicting the occurrence of PVL. The selected features were next
used as inputs to a decision tree (DT) algorithm for generating easily interpretable
rules of PVL prediction.
Results: Prediction performance for two CI based classiﬁers, MLP and PNN coupled
with GA are presented for different number of selected features. The best prediction
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