title: An intelligent system approach for asthma prediction in symptomatic preschool children.
abstract: OBJECTIVE
  In this study a new method for asthma outcome prediction, which is based on Principal Component Analysis and Least Square Support Vector Machine Classifier, is presented. Most of the asthma cases appear during the first years of life. Thus, the early identification of young children being at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority.
  
  
  METHODS
  The proposed intelligent system consists of three stages. At the first stage, Principal Component Analysis is used for feature extraction and dimension reduction. At the second stage, the pattern classification is achieved by using Least Square Support Vector Machine Classifier. Finally, at the third stage the performance evaluation of the system is estimated by using classification accuracy and 10-fold cross-validation.
  
  
  RESULTS
  The proposed prediction system can be used in asthma outcome prediction with 95.54 % success as shown in the experimental results.
  
  
  CONCLUSIONS
  This study indicates that the proposed system is a potentially useful decision support tool for predicting asthma outcome and that some risk factors enhance its predictive ability.
authors.affiliation: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece. echatzim@ee.duth.gr
authors.forename: E
authors.initials: E
authors.lastname: Chatzimichail
authors.forename: E
authors.initials: E
authors.lastname: Paraskakis
authors.forename: M
authors.initials: M
authors.lastname: Sitzimi
authors.forename: A
authors.initials: A
authors.lastname: Rigas
mesh_terms: D001249:Asthma / Q000175:diagnosis* / Q000503:physiopathology; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D060388:Support Vector Machine