title: Comparison of machine learning methods for classifying aphasic and non-aphasic speakers.
abstract: The performance of eight machine learning classifiers were compared with three aphasia related classification problems. The first problem contained naming data of aphasic and non-aphasic speakers tested with the Philadelphia Naming Test. The second problem included the naming data of Alzheimer and vascular disease patients tested with Finnish version of the Boston Naming Test. The third problem included aphasia test data of patients suffering from four different aphasic syndromes tested with the Aachen Aphasia Test. The first two data sets were small. Therefore, the data used in the tests were artificially generated from the original confrontation naming data of 23 and 22 subjects, respectively. The third set contained aphasia test data of 146 aphasic speakers and was used as such in the experiments. With the first and the third data set the classifiers could successfully be used for the task, while the results with the second data set were less encouraging. However, based on the results, no single classifier performed exceptionally well with all data sets, suggesting that the selection of the classifier used for classification of aphasic data should be based on the experiments performed with the data set at hand.
authors.affiliation: University of Tampere, Department of Information Studies and Interactive Media, FI-33014 University of Tampere, Finland. ajarvelin@gmail.com
authors.forename: Antti
authors.initials: A
authors.lastname: Järvelin
authors.forename: Martti
authors.initials: M
authors.lastname: Juhola
mesh_terms: D000544:Alzheimer Disease / Q000503:physiopathology; D001037:Aphasia / Q000503:physiopathology*; D001185:Artificial Intelligence; D006801:Humans; D013060:Speech; D014652:Vascular Diseases / Q000503:physiopathology