A new expert system for pediatric respiratory diseases by using neural networks

  • Authors:

  • Ahmed A. Radwan;Hazem M. El-Bakry;Hager M. El Hadad

  • Affiliations:

  • Faculty of Computer Science and Information Systems, Minia University, Minia, Egypt;Faculty of Computer Science and Information Systems, Mansoura University, Egypt;Faculty of Computer Science, Nahda University, Beni-Suef, Egypt

  • Venue:
  • AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
  • Year:
  • 2011

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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 study, 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. Using medical symptoms such as dry cough, productive cough, fever, heamoptysis, tachypnea, dysnea and etc. Also using doctor sign such as bronchial breathing, chest pain, clubbing of finger, crepitation, ronchi, cyanosis, decrease brearthing sound on auscultation, dullness on percussion, hyper resonant on percussion, inability to swallow, mucopurelent sputum, pleural rub, respiratory distress, sputum (white), stridor, upper respiratory infection, wheezing, X-ray [showing lung consolidation], X-ray [shawing edematous epiglottic], X-ray [shawing subglottic narrowing and classic narrow trachea], X-ray [showing hypertanslucent lung], X-ray [showing lobar collapse and (increase) bronchovascular marking], X-ray [showing diffuse haziness], it can predict the likelihood of patients getting a respiratory disease. They enable significant knowledge, e.g. patterns, relationships between medical factors related to respiratory disease, to be established.