Classification of Different Breath Samples Utilizing E-Nose System

In this work, by using an Electronic Nose, human breath samples were analyzed. Breaths samples were collected from three different groups. These are lung cancer patients, healthy people and healthy smokers. In structure of developed E-Nose, Quartz Crystal Microbalance and Metal Oxide Semiconductor gas sensors were used. Data acquired from system were preprocessed and dimension of these data were reduced with Linear Discriminant Analysis algorithm. Classification of data was performed with k-Nearest Neighbors, Support Vector Machines algorithms. For both algorithms, maximum classification success rates were obtained as 94.1% with 5 Fold Cross Validation.