上呼吸道感染月发病率的主成分神经网络预测模型

 在医学卫生领域，疾病受许多因素的影响，很难用结构式因果模型解释，根据神经网络预测是一种行之有效的方法。径向基函数（RBF）神经网络应用于疾病的月发病人数预测时，由于影响它的气象因素，如月平均气压、月平均气温、月平均相对湿度、月平均风速、月平均降水量等本身具有很大的相关性，且维数较高，RBF神经网络的预测精度会下降。针对这一问题，本文提出了利用主成分分析方法对原输入空间进行重构，并根据各主成分的贡献率确定网络结构，从而有效地解决了预测精度下降问题。最后，以2001年8月至2006年9月甘肃省武威市上呼吸道感染炎月发病人数的资料验证该方法的有效性。至此，应该充分考虑人在各时间段的发病特征，以便更有重点地进行健康防治工作，有效地降低支气管肺炎对人类的危害，保障人类的生活品质。

Principal Component Neural Network Prediction Model for Incidence of Upper Respiratory Tract Infection

In the medical field, due to the fact that diseases are often affected by many factors, it is difficult to use a structural causal model, while on the other hand, it would be effective to establish a dynamic model, based on their own time-series changes. To predict the number and incidence of diseases, because meteorological factors, the monthly average atmospheric pressure, monthly mean temperature, monthly mean relative humidity, monthly average wind speed, the monthly average precipitation are strongly correlated between themselves and with very high dimensions, the accuracy of the Radial Basis Function (RBF) neural network prediction may be very low. To solve this problem, this paper proposes the use of the Principal Component Analysis (PCA) method to reconstruct the original input space and, based on the contribution rate of all main components to determine the network structure, which will effectively solve the problem of low prediction accuracy. The incidence data of upper respiratory tract infection, from August 2001 to September 2006, in Wuwei City of Gansu Province are used to validate the method. The clinical characteristics in each time period should be duly considered in order to carry out a more focused health prevention and treatment and to effectively reduce the hazards to human bronchial pneumonia.