RESEARCH PAPER
Data Mining Techniques as a Tool in Neurological Disorders Diagnosis
1
Faculty of Mechanical Engineering, Department of Biocybernetics and Biomedical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Bialystok, Poland
2
Faculty of Medicine, Department of Neurology, Medical University of Bialystok, ul. M. Skłodowskiej-Curie 24A, 15-276 , Białystok, Poland
Submission date: 2018-01-25
Acceptance date: 2018-09-19
Online publication date: 2018-10-16
Publication date: 2018-09-01
Acta Mechanica et Automatica 2018;12(3):217-220
KEYWORDS
Data MiningClassification RulesDecision TreeAction RulesNeurological DisordersStrokeMultiple Sclerosis
ABSTRACT
Neurological disorders are diseases of the brain, spine and the nerves that connect them. There are more than 600 diseases of the nervous system, such as epilepsy, Parkinson's disease, brain tumors, and stroke as well as less familiar ones such as multiple sclerosis or frontotemporal dementia. The increasing capabilities of neurotechnologies are generating massive volumes of complex data at a rapid pace. Evaluating and diagnosing disorders of the nervous system is a complicated and complex task. Many of the same or similar symptoms happen in different combinations among the different disorders. This paper provides a survey of developed selected data mining methods in the area of neurological diseases diagnosis. This review will help experts to gain an understanding of how data mining techniques can assist them in neurological diseases diagnosis and patients treatment.
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| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
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