title: [Prediction of ventilatory function in children and adolescents using backpropagation neural networks].
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Chen
authors.forename: Zheng-Guo
authors.initials: ZG
authors.lastname: Zhang
authors.forename: Kui
authors.initials: K
authors.lastname: Feng
authors.forename: Li
authors.initials: L
authors.lastname: Chen
authors.forename: Shao-Mei
authors.initials: SM
authors.lastname: Han
authors.forename: Guang-Jin
authors.initials: GJ
authors.lastname: Zhu
mesh_terms: D000293:Adolescent; D002648:Child; D002681:China; D005260:Female; D005541:Forced Expiratory Volume / Q000502:physiology; D005544:Forecasting; D006801:Humans; D008168:Lung / Q000502:physiology*; D008297:Male; D016571:Neural Networks, Computer; D010366:Peak Expiratory Flow Rate / Q000502:physiology; D012123:Pulmonary Ventilation / Q000502:physiology*; D012016:Reference Values; D012494:Sampling Studies; D014797:Vital Capacity / Q000502:physiology