Multimodal Learning Determines Rules of Disease Development in Longitudinal Course with Parkinson’s Patients

  • Andrzej W. Przybyszewski
  • Stanislaw Szlufik
  • Piotr Habela
  • Dariusz M. Koziorowski
Part of the Studies in Big Data book series (SBD, volume 40)


Parkinson’s disease (PD) is neurodegenerative disease (ND) related to the lost of dopaminergic neurons that elevates first by motor and later also by non-motor (dementia, depression) disabilities. Actually, there is no cure for ND as we are not able to revive death cells. Our purpose was to find, with help of data mining and machine learning (ML), rules that describe and predict disease progression in two groups of PD patients: 23 BMT patients that are taking only medication; 24 DBS patients that are on medication and on DBS (deep brain stimulation) therapies. In the longitudinal course of PD there were three visits approximately every 6 months with the first visit for DBS patients before electrode implantation. We have estimated disease progression as UPDRS (unified Parkinson’s disease rating scale) changes on the basis of patient’s disease duration, saccadic eye movement parameters, and neuropsychological tests: PDQ39, and Epworth tests. By means of ML and rough set theory we found rules on the basis of the first visit of BMT patients and used them to predict UPDRS changes in next two visits (global accuracy was 70% for both visits). The same rules were used to predict UPDRS in the first visit of DBS patients (global accuracy 71%) and the second (78%) and third (74%) visit of DBS patients during stimulation-ON. These rules could not predict UPDRS in DBS patients during stimulation-OFF visits. In summary, relationships between condition and decision attributes were changed as result of the surgery but restored by electric brain stimulation.


Neurodegenerative disease Rough set Decision rules Granularity 



This work was partly supported by projects Dec-2011/03/B/ST6/03816, from the Polish National Science Centre.


  1. 1.
    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). Scholar
  2. 2.
    Przybyszewski, A.W.: Logical rules of visual brain: From anatomy through neurophysiology to cognition. Cogn. Syst. Res. 11, 53–66 (2010)CrossRefGoogle Scholar
  3. 3.
    Przybyszewski, A.W., Kon, M., Szlufik, et al.: Data mining and machine learning on the basis from reflexive eye movements can predict symptom development in individual Parkinson’s patients. In: Gelbukh et al. (eds.) Nature-Inspired Computation and Machine Learning, pp. 499–509. Springer (2014)Google Scholar
  4. 4.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Dordrecht (1991); Springer, pp. 499–509 (2014)CrossRefGoogle Scholar
  5. 5.
    Bazan, J., Nguyen, H.Son, Nguyen, Trung T., Skowron, A., Stepaniuk, J.: Desion rules synthesis for object classification. In: Orłowska, E. (ed.) Incomplete Information: Rough Set Analysis, pp. 23–57. Physica-Verlag, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T. (eds.) Rough Set Methods and Applications, pp. 49–88. Physica-Verlag, Heidelberg, New York (2000)CrossRefGoogle Scholar
  7. 7.
    Grzymała-Busse, J.: A new version of the rule induction system LERS. Fundamenta Informaticae 31(1), 27–39 (1997)zbMATHGoogle Scholar
  8. 8.
    Bazan, J., Szczuka, M.: The rough set exploration system. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56 (2005)CrossRefGoogle Scholar
  9. 9.
    Bazan, J., Szczuka, M.: RSES and RSESlib—a collection of tools for rough set computations. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000, LNAI 2005, pp. 106−113 (2001)CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Andrzej W. Przybyszewski
    • 1
    • 3
  • Stanislaw Szlufik
    • 2
  • Piotr Habela
    • 1
  • Dariusz M. Koziorowski
    • 2
  1. 1.Polish-Japanese Academy of Information TechnologyWarsawPoland
  2. 2.Faculty of Health Science, Department of NeurologyMedical University WarsawWarsawPoland
  3. 3.Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterUSA

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