NEURAL NETWORK–BASED CLASSIFICATION OF NORMAL AND ABNORMAL PULMONARY FUNCTION USING SPIROMETRIC MEASUREMENTS

Abstract

In this work, the classification of pulmonary function into normal and abnormal conditions is attempted using neural networks and spirometric measurements. The pulmonary function data (N = 229) for this study were obtained from volunteers using commercially available spirometers by adopting standard data acquisition protocol and recording conditions. The data were then subjected to neural network–based training (N = 159) and analysis (N = 70). The classification was carried out using standard feed-forward neural network and backpropagation algorithm. The outputs were then validated through sensitivity and specificity measurements together with clinical observation. The results confirmed the effectiveness of the neural network–based classification of spirometric data into normal and abnormal conditions. The sensitivity and specificity were found to be 89.25% and 82.25%, respectively. Furthermore, it seems that this method is useful in assessing the pulmonary function dynamics in cases with incomplete data and data with poor recordings. In this paper, the methodology, data collection procedure, and neural network–based analysis and results are described in detail.