Genetic-Evolved Bayesian Networks in a Biomedical Application

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 20)

Abstract

This study presents genetic algorithm (GA) for discovering Bayesian network structure. The algorithm is applied to a medical datasets for vertebral column. Data set containing values for six biomechanical features is used to classify patients into three categories: disk hernia (DH), spondylolisthesis (SL), and normal (NO) or two categories: abnormal (AB), and NO. On ten-fold cross-validation run, the average AUC (the area under the ROC curve) measures of 0.874 and 0.923 for two and three categories are obtained, respectively. Results indicate that GA is relatively effective algorithm. Consequently, the GA-evolved BN is powerful tool for knowledge representation and inference because the causality relationship can be observed.

Keywords

Bayesian network Genetic algorithm Medicine Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Digit Fashion DesignToko UniversityPu-Tzu CityTaiwan

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