Neural networks for the prediction of spirometric reference values.

Normal lung function values are conventionally calculated according to prediction equations. The primary objective of this study is the development of a different method for the prediction of FVC and FEV1 parameters, in order to achieve better correlation of the predicted values to the real ones. Using a sample from the Greek elderly population that was separated into two groups (a training and a testing one), a number of artificial neural networks were trained. Considering that men and women were studied separately and that two parameters (FVC, FEV1) were the target of the study, four cases came up. In each case two neural networks were trained using different transfer functions, number of neurons and number of layers. When passing the inputs of the testing data set to the trained networks it was found that the outputs were well correlated with the corresponding measures of the sample. Furthermore, the match with the sample, for a number of neural networks developed, was better compared to the matches of Baltopoulos et al. study that used the same sample for developing prediction equations. This high match allows the potential use of neural networks for predicting not only FVC and FEV1 but also other spirometric parameters.