Data mining techniques as a tool in neurological disorders diagnosis

• Autorzy: Zdrodowska M. , Dardzińska A. , Chorąży M. , Kułakowska A.

Identyfikatory

DOI

10.2478/ama-2018-0033

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

data mining; classification rules; decision tree; action rules; neurological disorders; stroke; multiple sclerosis

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2018
• Tom: Vol. 12, no. 3
• Strony: 217--220
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Zdrodowska M.

• Faculty of Mechanical Engineering, Department of Biocybernetics and Biomedical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Bialystok, Poland

autor

Dardzińska A.

• Faculty of Mechanical Engineering, Department of Biocybernetics and Biomedical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Bialystok, Poland

autor

Chorąży M.

• Faculty of Medicine, Department of Neurology, Medical University of Bialystok, ul. M. Skłodowskiej-Curie 24A, 15-276 Białystok, Poland

autor

Kułakowska A.

• Faculty of Medicine, Department of Neurology, Medical University of Bialystok, ul. M. Skłodowskiej-Curie 24A, 15-276 Białystok, Poland

Bibliografia

• 1. Acquarelli J., The Netherlands Brain Bank, Bianchini M., Marchiori E. (2016), Discovering Potential Clinical Profiles of Multiple Sclerosis from Clinical and Pathological Free Text Data with Constrained Non-negative Matrix Factorization, In: Squillero G., Burelli P. (editors), Applications of Evolutionary Computation, Lecture Notes in Computer Science, Springer, Cham, 9597, 169–183.
• 2. Bejarano H.B., Bianco M., Gonzalez-Moron D. (2011), Computational classifiers for predicting the short-term course of Multiple Sclerosis, BMC Neurology, 11:67.
• 3. Bejarano H.B., Segura V., Villoslada P. (2013), Data mining in multiple sclerosis: computational classifiers. Introduction and methods (Part I), Revista Española de Esclerosis Múltiple, 5, 5–15.
• 4. Carreiro A.V., Anunciação O., Carriço J.A., Madeira S.C. (2011), Biclustering-Based Classification of Clinical Expression Time Series: A Case Study in Patients with Multiple Sclerosis, In: Rocha MP., Rodríguez JMC., Fdez-Riverola F, Valencia A. (editors), 5th International Conference on Practical Applications of Computational Biology & Bioinformatics, Advances in Intelligent and Soft Computing, Springer, Berlin, Heidelberg, 93.
• 5. Dardzinska A. (2013), Action rules mining, Springer-Verlag, Berlin.
• 6. Dardzinska A., Romaniuk A. (2016), Mining of Frequent Action Rules, In: Ryżko D, Gawrysiak P, Kryszkiewicz M, Rybiński H. (editors), Machine Intelligence and Big Data in Industry, Studies in Big Data, Springer, Cham, 19, 87-95.
• 7. Han J., Kamber M. (2006), Data mining. Concepts and Techniques, 2 nd ed, Elsevier, San Francisco.
• 8. Jacobs L.K., Sapers B.L. (2011), Neurological Disease, In: Cohn S. (editor), Perioperative Medicine, Springer, London.
• 9. Kozubski W., Liberski P. (2003), Neurological diseases (in Polish), Wydawnictwo Lekarskie, Warsaw.
• 10. Larose D.T. (2005), Discovering knowledge in data. An introduction to data mining, John Wiley & Sons, Inc., New Jersey.
• 11. Lavrač N., Zupan B. (2010) Data Mining in Medicine, In: Maimon O., Rokach L. (editors), Data Mining and Knowledge Discovery Handbook, Springer, Boston.
• 12. Ludwin S.K., Antel J., Arnold D.L. (2016), Multiple Sclerosis, In: Pfaff D., Volkow N. (editors), Neuroscience in the 21st Century, Springer, New York.
• 13. Pappa G.L., Freitas A.A. (2010), Automating the design of data mining algorithms. An evolutionary computation approach, Springer – Verlag, Berlin.
• 14. Raś Z.W., Dardzińska A. (2008a), Action Rules Discovery Based on Tree Classifiers and Meta-actions, In: Rauch J., Raś Z.W., Berka P., Elomaa T. (editors), Foundations of Intelligent Systems, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 5722.
• 15. Raś Z.W., Dardzińska A. (2008b), Action Rules Discovery without Pre-existing Classification Rules, In: Chan C.C., Grzymala-Busse J.W., Ziarko W.P. (editors), Rough Sets and Current Trends in Computting, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 5306.
• 16. Raś Z.W., Dardzinska A., Tsay L.-S., Wasyluk H. (2008), Association Action Rules, IEEE/ICDM Workshop on Mining Complex Data (MCD 2008), 283–290.
• 17. Raś Z.W., Wieczorkowska A. (2000), Action-Rules: How to Increase Profit of a Company, In: Zighed D.A., Komorowski J., Żytkow J. (editors), Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 1910.
• 18. Rodríguez J.P., Aritz P., Arteta D., Tejedor D., Lozano J.A. (2012), Using Multi-Dimensional Bayesian Network Classifiers to Assist the Treatment of Multiple Sclerosis, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 42, 1705–1715.
• 19. Snarska K.K., Bachórzewska-Gajewska K., Kapica-Topczewska K., Drozdowski W., Chorąży M., Kułakowska A., Małyszko J. (2016), Hyperglycemia and diabetes have different impacts on outcome of ischemic and hemorrhagic stroke, Archives of Medical Science, 13(1), 100–108.
• 20. Triantaphyllou E., Felici G. (editors) (2006), Data mining and knowledge discovery approaches based on rule induction techniques, Springer Science+Business Media, New York.
• 21. Trochimczyk A., Chorąży M., Snarska K.K. (2017), An Analysis of Patient Quality of Life after Ischemic Stroke of the Brain, The Journal of Neurological and Neurosurgical Nursing, 6(2), 44–54.
• 22. World Health Organization (2006), Neurological disorders: public health challenges, Geneva.
• 23. Yamashita T., Deguchi K., Sehara Y., Lukic-Panin V., Zhang H., Kamiya T., Abe K. (2009), Therapeutic strategy for ischemic stroke, Neurochemical Research, 34, 707–710.

Uwagi

Acknowledgements: This work is supported by the Ministry of Science and Higher Education of Poland under research project No. MB/WM/17/2018.