Abstract
We have already demonstrated that measurements of eye movements in Parkinson’s disease (PD) are diagnostic. We have performed experimental measurements of fast reflexive saccades (RS) in PDs in order to predict effects of different therapies. We have also found rules by means of data mining and machine learning (ML) in order to classify how different doses of medication have determined motor symptoms (UPDRS III) improvements. These rules from one group of 23 patients only on medications were supplied to another group of 18 patients under medications and DBS (deep brain stimulation) therapies in order to predict motor symptoms changes. Such parameters as patient’s age, neurological and saccade’s parameters gave a global accuracy in the motor symptoms predictions of 76% based on the cross-validation. Our approach demonstrated that rough set rules are universal between groups of patients with different therapies that may help to predict optimal treatments for individual PDs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Przybyszewski, A.W.: Logical rules of visual brain: from anatomy through neurophysiology to cognition. Cogn. Syst. Res. 11, 53–66 (2010)
Przybyszewski, A.W., Kon, M., Szlufik, S., Szymanski, A., Koziorowski, D.M.: Multimodal learning and intelligent prediction of symptom development in individual Parkinson’s patients. Sensors 16(9), 1498 (2016). doi:10.3390/s16091498
Przybyszewski, A.W., Kon, M., Szlufik, S., Dutkiewicz, J., Habela, P., Koziorowski, D.M.: Data mining and machine learning on the basis from reflexive eye movements can predict symptom development in individual Parkinson’s patients. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds.) MICAI 2014. LNCS (LNAI), vol. 8857, pp. 499–509. Springer, Heidelberg (2014). doi:10.1007/978-3-319-13650-9_43
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data, pp. 499–509. Kluwer, Springer, Dordrecht (1991, 2014)
Bazan, J., Son Nguyen, H., Trung, T., Nguyen, Skowron A., Stepaniuk, J.: Decision rules synthesis for object classification. In: Orłowska, E. (ed.) Incomplete Information: Rough Set Analysis, pp. 23–57. Physica-Verlag, Heidelberg (1998)
Bazan, J.G., Szczuka, M.: RSES and RSESlib - a collection of tools for rough set computations. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 106–113. Springer, Heidelberg (2001). doi:10.1007/3-540-45554-X_12
Acknowledgements
This work was partly supported by projects Dec-2011/03/B/ST6/03816, from the Polish National Science Centre.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Przybyszewski, A.W., Szlufik, S., Habela, P., Koziorowski, D.M. (2017). Rules Found by Multimodal Learning in One Group of Patients Help to Determine Optimal Treatment to Other Group of Parkinson’s Patients. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-54430-4_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54429-8
Online ISBN: 978-3-319-54430-4
eBook Packages: Computer ScienceComputer Science (R0)