Application of artificial intelligence in the diagnosis of pulmonary emphysema
Pulmonary emphysema is a complicated disease caused by irreversible damage to the wall of the pulmonary alveoli and causes 5% of the total mortality worldwide. This paper presents the development of an artificial neural network (ANN) for the diagnosis of pulmonary emphysema. Following biomarkers were used for the development of the ANN: AAT (alphal-antitrypsin), FEV1 (forced expiratory volume in 1 second), FVC (forced vital capacity) and FEV1/FVC (ratio forced expiratory volume in 1 second / forced vital capacity). The dataset consisted of 300 patients: 210 healthy subjects and 90 subject with disease. The neural network has 4 input parameters and 1 output parameter. For the final architecture, a neural network with 13 neurons in hidden layer was chosen based on the training results. The developed ANN has shown good performance and has a potential for use in this field.