الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The successful application of data mining in highly visible fields like e-business and marketing have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data mining are the fields of medicine and public health. The medical industries collect huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. We can describe this data as being ‘information rich’ yet ‘knowledge poor.’
In this work, we briefly examine the use of the most important data mining techniques such as Artificial Neural Network to massive volume of data in medical field which is pediatric respiratory disease. After we had trained the network with 699 cases contains input vector and target vector, we tested it with 20 cases without the target vector. The output of the tested cases is one of the following eight examinations (bronchiolitis, pneumonia, acute epiglottitis, pleurisy, emphysema, acute laryngotracheobronchitis, bronchial asthma and bronchiectasis).
The present data explained that 90% of all test cases represent the correct examination. This means that the experimental results on this medical data illustrate that neural networks are important in Diagnosis of medical data, especially for a large amount of data in a high-dimensional space.
Also we design an intelligent algorithm for automatic recognition of pediatric respiratory diseases. Data mining Cluster analysis has been widely used in several disciplines, such as statistics, software engineering, biology, psychology and other social sciences, in order to identify natural groups in large amounts of data.
In addition we briefly examine the implementation of both Principle Component Technique and Self Organization map (SOM) clustering techniques to massive volume of medical data in one of medical field which is pediatric respiratory disease. Then we compare results of SOM cluster technique with the result of Principal Components Analysis cluster process (PCA). The simulation results show that SOM performs better than PCA recognizing.