摘要
为早期诊断川崎病,应用BP神经网络原理建立川崎病的诊断模型。以156例川崎病与非川崎病患者的体温、皮疹、口腔黏膜改变、实验室检查结果等9项指标等作为BP神经网络的输入参数,在MATLAB7程序中对其中随机抽取的90例学习样本进行训练并建模。以剩余的66例作为测试样本进行预测,结果表明该模型对川崎病和非川崎病的预测准确率分别为97.4%、92.9%,提示此模型可有效地判别出川崎病与非川崎病,可用于川崎病的早期辅助诊断。
In order to diagnose Kawasaki Disease during early phase,clinical symptoms(temperature,rash,conjunctival injection,erythema of thelips,and oral mucosal changes) and laboratory data(white blood cell,neutrophil,platelet,c-reactive protein,and erythrocyte sedimentation rate) of 156 children with Kawasaki disease or infectious diseases were used to develop a BP neural network model.90 random cases were trained using MATLAB software for setting up the BP neural network model.The other 66 cases were analyzed to predict diagnosis of Kawasaki disease using this model.Results showed that the predict accuracy in patients with Kawasaki disease and children with infectious diseases were 97.4% and 92.9%,respectively.Our result indicates that the BP neural network model is likely to provide an accurate test for early diagnosis of Kawasaki disease.
出处
《生物医学工程研究》
2011年第4期207-210,共4页
Journal Of Biomedical Engineering Research
基金
广东省自然科学基金资助项目(S2011040003573)
关键词
BP神经网络
川崎病
诊断
BP neural network
Kawasaki disease
Diagnosis
