Abstract
The purpose of this thesis is to the recognize Chronic Obstructive Pulmonary Disease (COPD) by applying machine-learning algorithms. In previous literature, it is confirmed that specific organic compounds are exhaled by most patients suffering from the COPD. The COPD could thus be diagnosed by using machine-learning algorithms to classify the sensory data of an electronic nose. An electronic nose (e-Nose) consists of an array of neuromorphic sensor with diversity. Each sensor exhibits its own characteristic response to different odorants. Therefore, this study aims to identify a machine-learning algorithm able to detect COPD by classifying the sensory data of an e-Nose. To ease data-classification, the following methods are employed to preprocess the e-Nose data: (1) baseline manipulation, (2) receiver operating characteristic (ROC) curve, and (3) normalization. For data classification, the performance of the following three linear classifiers are compared: (1) the support vector machine, (2) the linear discriminant analysis, (3) the linear programming. In addition, the Continuous Restricted Boltzmann Machine (CRBM) is employed as a nonlinear, probabilistic classifier. How the CRBM could improve the classification task is further explored in this thesis. Based on the fact that the CRBM learns to regenerate training data, an algorithm for estimating the likelihood of unknown data under a CRBM model is developed. This estimating algorithm enables CRBM to function as a probabilistic classifier reliably. However, our experimental results indicate that all algorithms are unable to recognize unknown data because different types of pre-processed COPD data exhibit significant overlap among each other. Further analysis indicates that sensor selection based on ROC curve filters out some important dimensions. Therefore, without the sensor selection, better classification result is achieved.