Department of Electrical and Electronics Engineering, Sakarya University, 54187 Adapazari, Turkey.
J Med Syst. 2009 Dec;33(6):485-92. doi: 10.1007/s10916-008-9209-x.
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.
每年全球都有数百万人被诊断患有胸部疾病。慢性阻塞性肺疾病和肺炎是两种最重要的胸部疾病。而这些疾病在土耳其非常常见。在本文中，使用神经网络和人工免疫系统对慢性阻塞性肺疾病和肺炎的诊断进行了比较研究。为此，使用了三种不同的神经网络结构和一个人工免疫系统。本研究中使用的神经网络结构是多层、概率和学习向量量化神经网络。研究结果与之前专注于慢性阻塞性肺疾病和肺炎诊断的类似研究的结果进行了比较。慢性阻塞性肺疾病和肺炎数据集是使用患者的病历报告从一家胸部疾病医院的数据库中准备的。
