儿童青少年肺通气功能预测的后向传播神经网络方法

本文旨在研究儿童青少年肺通气功能预测的后向传播神经网络(backpropagation neural network,BPNN)方法,以期得到更准确的肺通气功能预计值。样本数据包括内蒙古自治区10~18岁汉族健康儿童青少年999人(男性500人,女性499人),测量身高和体重,使用肺功能仪检测肺通气功能。利用BPNN和多元逐步回归,对用力肺活量(forced vital capacity,FVC)、用力呼气一秒量(forced expiratory volume in one second,FEV1)、最大呼气流量(peak expiratory flow,PEF)、用力呼出25%肺活量时呼气流量(forced expiratory flow at25%of forced vital capacity,FEF25%)、用力呼出50%肺活量时呼气流量(forced expiratoryflow at50%of forced vital capacity,FEF50%)、最大呼气中段流量(maximal mid-expiratory flow,MMEF)、用力呼出75%肺活量时呼气流量(forced expira...

Prediction of ventilatory function in children and adolescents using backpropagation neural networks

The aim of this study is to develop backpropagation neural networks (BPNN) for better prediction of ventilatory function in children and adolescents. Nine hundred and ninety-nine healthy children and adolescents (500 males and 499 females) aged 10-18 years, all of the Han Nationality, were selected from Inner Mongolia Autonomous Region, and their heights, weights, and ventilatory functions were measured respectively by means of physical examination and spirometric test. Using the approaches of BPNN and stepwise multiple regression, the prediction models and equations for forced vital capacity (FVC), forced expiratory volume in one second (FEV1), peak expiratory flow (PEF), forced expiratory flow at 25% of forced vital capacity (FEF25%), forced expiratory flow at 50% of forced vital capacity (FEF50%), maximal mid-expiratory flow (MMEF) and forced expiratory flow at 75% of forced vital capacity (FEF75%) were established. Through analyzing mean squared difference (MSD) and correlation coefficient (R) of the ventilatory function indexes, the present study compared the results of BPNN, linear regression equation based on this work (LR's equation), prediction equations based on the studies of Ip et al. (Ip's equation) and Zapletal et al. (Zapletal's equation). The results showed, regardless of sex, the BPNN prediction models appeared to have smaller MSD and higher R values, compared with those from the other prediction equations; and the LR's equation also had smaller MSD and higher R values compared with those from Ip's and Zapletal's equations. The coefficients of variance (CV) for FEF50%, MMEF and FEF75% were higher than those of the other ventilatory function parameters, and their increasing percentages of R values (ΔR, relative to R values by LR's equation) derived by BPNN were correspondingly higher than those of the other indexes. In sum, BPNN approach for ventilatory function prediction outperforms the traditional regression methods. When CV of a certain ventilatory function parameter is higher, the superiority of BPNN would be more significant compared with traditional regression methods.