Assessment of Multi-Layer Perceptron Neural Network for Pulmonary Function Test’s Diagnosis Using ATS and ERS Respiratory Standard Parameters

Abstract

The aim of the research work is to investigate the operability of the entire 23 pulmonary function parameters, which are stipulated by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), to design a medical decision support system capable of classifying the pulmonary function tests into normal, obstructive, restrictive, or mixed cases. The 23 respiratory parameters specified by the ATS and the ERS guidelines, obtained from the Pulmonary Function Test (PFT) device, were employed as input features to a Multi-Layer Perceptron (MLP) neural network. Thirteen possible MLP Back Propagation (BP) algorithms were assessed. Three different categories of respiratory diseases were evaluated, namely obstructive, restrictive, and mixed conditions. The framework was applied on 201 PFT examinations: 103 normal and 98 abnormal cases. The PFT decision support system’s outcomes were compared with both the clinical truth (physician decision) and the PFT built-in diagnostic software. It yielded 92–99% and 87–92% accuracies on the training and the test sets, respectively. An 88–94% area under the receiver operating characteristic curve (ROC) was recorded on the test set. The system exceeded the performance of the PFT machine by 9%. All 23 ATS\ERS standard PFT parameters can be used as inputs to design a PFT decision support system, yielding a favorable performance compared with the literature and the PFT machine’s diagnosis program.