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Mining balance disorders' data for the development of diagnostic decision support systems

Exarchos, T. P., Rigas, G., Bibas, A., Kikidis, D., Nikitas, C., Wuyts, F. L., Ihtijarevic, B., Maes, L., Cenciarini, M., Maurer, C., Macdonald, N., Bamiou, D. E., Luxon, L., Prasinos, M., Spanoudakis, G., Koutsouris, D. and Fotiadis, D. I. (2016). Mining balance disorders' data for the development of diagnostic decision support systems. Computers in Biology and Medicine, 77, pp. 240-248. doi: 10.1016/j.compbiomed.2016.08.016

Abstract

In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts.

Publication Type: Article
Additional Information: © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Balance disorders, Data mining, Decision support systems, Vestibular system
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/15490
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