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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 240182, 6 pages
Research Article

An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

1Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
2Department of Pediatrics, Democritus University of Thrace, 68100 Alexandroupolis, Greece

Received 26 October 2012; Accepted 21 February 2013

Academic Editor: Angel García-Crespo

Copyright © 2013 E. Chatzimichail et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objectives. 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.