Automatic Diagnosis of Parkinson’s Disease Using Machine Learning : A Comparative Study of Different Feature Selection Algorithms, Classifiers and Sampling Methods

Sammanfattning: Over the past few years, several studies have been published to propose algorithms for the automated diagnosis of Parkinson’s Disease using simple exams such as drawing and voice exams. However, at the same time as all classifiers appear to have been outperformed by another classifier in at least one study, there appear to lack a study on how well different classifiers work with a certain feature selection algorithm and sampling method. More importantly, there appear to lack a study that compares the proposed feature selection algorithm and/or sampling method with a baseline that does not involve any feature selection or oversampling. This leaves us with the question of which combination of feature selection algorithm, sampling method and classifier is the best as well as what impact feature selection and oversampling may have on the performance. Given the importance of providing a quick and accurate diagnosis of Parkinson’s disease, a comparison is made between different systems of classifier, feature selection and sampling method with a focus on the predictive performance. A system was chosen as the best system for the diagnosis of Parkinson’s disease based on its comparative predictive performance on two sets of data - one from drawing exams and one from voice exams.