【摘要】 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。更多还原

【Abstract】 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.更多还原