• Orhan Er, Cengiz Sertkaya, Feyzullah Temurtas, and A Cetin Tanrikulu.
• Department of Electrical and Electronics Engineering, Sakarya University, 54187 Adapazari, Turkey.
• J Med Syst. 2009 Dec 1; 33 (6): 485-92.

AbstractMillions 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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