A study on chronic obstructive pulmonary disease diagnosis using multilayer neural networks

Chronic Obstructive Pulmonary Disease (COPD) is a disease state characterized by airflow limitation that is not fully reversible. The airflow limitation is usually both progressive and associated with an abnormal inflammatory response of the lungs to noxious particles or gases. COPD is important health problem and one of the most common illnesses in Turkey. It is generally accepted that cigarette smoking is the most important risk factor and genetic factors are believed to play a role in the individual susceptibility. In this study, a study on COPD diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. Back propagation with momentum and Levenberg-Marquardt algorithms were used for the training of the neural networks. The COPD dataset were prepared from a chest diseases hospital's database using patient's epicrisis reports.