CAJ | 학술논문

选取协和医院64例结节病和42例肺结核病病历作为数据样本,探究建立基于LVQ神经网络的临床辅助鉴别诊断模型以实现这两种疾病的鉴别诊断,测试结果总体确诊率达到87.50%,并与BP神经网络算法结果相对比.实证分析结果表明,该模型对疾病的辅助鉴别诊断有着良好的应用效果,利用LVQ神经网络实现疾病的临床辅助鉴别诊断是有效的.
선취협화의원64례결절병화42례폐결핵병병력작위수거양본,탐구건립기우LVQ신경망락적림상보조감별진단모형이실현저량충질병적감별진단,측시결과총체학진솔체도87.50%,병여BP신경망락산법결과상대비.실증분석결과표명,해모형대질병적보조감별진단유착량호적응용효과,이용LVQ신경망락실현질병적림상보조감별진단시유효적.
This paper put forward a clinical auxiliary differential diagnosis model based on learning vector quantization(LVQ) neural network.An experiment was designed with the data sample of 106 medical records consisting of 64 patients with sarcoidosis and 42 patients with tuberculosis from the Union Hospital. The empirical analysis results show that the test results have an overall positive rate of 87.50%, and the model for the clinical auxiliary differential diagnosis of diseases has a good application effect. The clinical auxiliary differential diagnosis based on LVQ neural network is appropriate, and it can make differential diagnosis of diseases effectively.