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A Comparative Study on Chronic Obstructive Pulmonary and Pneumonia Diseases Diagnosis using Neural Networks and Artificial Immune System

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Abstract

Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary and pneumonia diseases are two of the most important chest diseases. And these are very common illnesses in Turkey. In this paper, a comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis was realized by using neural networks and artificial immune systems. For this purpose, three different neural networks structures and one artificial immune system were used. Used neural network structures in this study were multilayer, probabilistic, and learning vector quantization neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on chronic obstructive pulmonary and pneumonia diseases diagnosis. The chronic obstructive pulmonary and pneumonia diseases dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Sakarya University, 54187, Adapazari, Turkey

    Orhan Er & Feyzullah Temurtas

  2. Department of Computer Engineering, Sakarya University, 54187, Adapazari, Turkey

    Cengiz Sertkaya

  3. Department of Electrical and Electronics Engineering, Bozok University, 66200, Yozgat, Turkey

    Feyzullah Temurtas

  4. Department of Chest Diseases, Faculty of Medicine, Sutcu Imam University, 46100, Kahramanmaras, Turkey

    A. Cetin Tanrikulu

Authors
  1. Orhan Er
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  2. Cengiz Sertkaya
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  3. Feyzullah Temurtas
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  4. A. Cetin Tanrikulu
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Correspondence to Feyzullah Temurtas.

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Er, O., Sertkaya, C., Temurtas, F. et al. A Comparative Study on Chronic Obstructive Pulmonary and Pneumonia Diseases Diagnosis using Neural Networks and Artificial Immune System. J Med Syst 33, 485–492 (2009). https://doi.org/10.1007/s10916-008-9209-x

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  • DOI: https://doi.org/10.1007/s10916-008-9209-x

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