Associating gender, obesity and smoking with the pulmonary function tests by using artificial neural networks

With the latest technological developments and increasing level of usage these technologies in medicine, nowadays biomedical systems become more important in the diagnosis of diseases. The analysis of the recorded physiological signals with the help of biomedical systems made it possible to obtain more useful and detailed information about the body areas related to these signals. Early and accurate diagnosis of respiratory problems is having great importance for many related treatments. Measuring of respiratory functions is required to detect pulmonary abnormalities. Various techniques are used to determine how the lungs perform breathing and how much air the lungs hold and how efficient they exchange oxygen and carbon dioxide. Pulmonary function tests (PFT) help doctors measure how efficiently the lungs perform. Spirometry is a widely preferred method of measuring lung function. A database has been created from the parameters taken from the institution that collects PFT data of volunteers. Institution is collecting data order to follow up and diagnose some diseases. In this study, it was aimed to determine smoking habits of volunteer participant with their PFT results. The interrelation of these two structures to the numerical data was carried out by artificial neural networks.