Luukka Pasi, Leppälampi Tapio
Laboratory of Applied Mathematics, Lappeenranta University of Technology, FIN-53851 LPR, Finland.
Comput Biol Med. 2006 Sep;36(9):1026-40. doi: 10.1016/j.compbiomed.2005.05.008. Epub 2005 Sep 12.
A new approach based on fuzzy similarity was presented for the detection of erythemato-squamous diseases, diabetes, liver disorders, breast cancer and thyroid. The domain contained records of patients with known diagnoses. The results were very promising with all data sets and some conclusions can be drawn that a fuzzy similarity model can be used for the diagnosis of patients taking into consideration the error rate. A fuzzy similarity classifier was used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. The results confirmed that the proposed model has potential in detecting erythemato-squamous diseases. The fuzzy similarity model achieved accuracy rates (over 97%) which were higher than that of the stand-alone neural network model or the ANFIS model suggested in [E.D. Ubeyli, I. Güler, Comput. Biol. Med. 35(5) (2005) 421-433]. With PIMA Indian diabetes, the detection model has an error rate of about 25% which is much better than the overall rate of 33% for diabetes. The model was also tested with other data sets: thyroid and two breast cancer data sets where the average detection accuracy was over 96% for all cases, which is quite good. Also, the liver disorder data set gave promising results.
提出了一种基于模糊相似度的新方法，用于检测红斑鳞屑性疾病、糖尿病、肝脏疾病、乳腺癌和甲状腺疾病。该领域包含已知诊断患者的记录。所有数据集的结果都很有前景，可以得出一些结论：考虑到错误率，模糊相似度模型可用于患者诊断。当将定义六种疾病指征的34个特征用作输入时，使用模糊相似度分类器来检测六种红斑鳞屑性疾病。结果证实，所提出的模型在检测红斑鳞屑性疾病方面具有潜力。模糊相似度模型的准确率（超过97%）高于[E.D. 于贝伊利，I. 居勒，《计算机生物学与医学》35(5) (2005) 421 - 433]中建议的独立神经网络模型或自适应神经模糊推理系统（ANFIS）模型。对于皮马印第安人糖尿病，检测模型的错误率约为25%，远优于糖尿病33%的总体错误率。该模型还使用其他数据集进行了测试：甲状腺和两个乳腺癌数据集，所有病例的平均检测准确率超过96%，相当不错。此外，肝脏疾病数据集也给出了有前景的结果。
