Performance of Different Machine Learning Methods for Sinus Diseases Classification

In this project, a sinonasal diseases dataset is created with the help of Ear, Nose, and Throat (ENT) specialists at King Abdullah University Hospital (KAUH), Jordan. This dataset is then used to experiment with different features extraction and selection methods and different machine learning classification methods. The work can be summarized as follows. We start by selecting the participating patients according to their history information that is acquired from the patients using a questionnaire. Then, we construct a tool that employs image processing techniques to process the selected patients' CT scan images to extract useful information from them. After that, different filters and wrapper selection methods with various machine learning techniques are utilized to classify the cases in the dataset. The results show that the performance of wrapper feature selection (using PART classifier with best first search) with all used classifiers produces better results compared with the other feature selection methods.