title: A hierarchical fuzzy rule-based approach to aphasia diagnosis.
abstract: Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.
authors.affiliation: Department of Biomedical Engineering, Islamic Azad University of Mashhad, Mashhad, Iran. akbarzadeh@ieee.org <akbarzadeh@ieee.org>
authors.forename: Mohammad-R
authors.initials: MR
authors.lastname: Akbarzadeh-T
authors.forename: Majid
authors.initials: M
authors.lastname: Moshtagh-Khorasani
mesh_terms: D000465:Algorithms; D001037:Aphasia / Q000145:classification* / Q000175:diagnosis*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity