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Parkinson’s Disease Development Prediction by C-Granule Computing

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Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11683))

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Abstract

Both Rough Set Theory (RST) and Fuzzy Rough Set Theory (FRST) are related to intelligent granular computing (GrC) but primary with help of static granules. Our granules are sets of attributes measured from Parkinson’s disease (PD) patient in a certain moment of his/her disease. In order to look into PD development in time during our longitudinal study, we have introduced the complex granule (c-granule) approach with properties of granules that are evolving with disease progression.

We have used a RST/FRST approach in order to find similarities between attributes of different patients in different disease stages to another group of more advanced PD patients. We have compared group (G1) of 23 PD with attributes measured three times (visits V1 to V3) every half of the year (G1V1, G1V2, G1V3) to other group of 24 more advanced PD (G2V1). By means of RST/FRST we have found rules describing symptoms of G2V1 and applied them to G1V1, G1V2, and G1V3. With RST (FRST) we’ve got the following accuracies: G1V1 – 59 (38)%; G1V2 – 68(54)%; G1V3 – 86(61)% but global coverage for FRST was better. This means that c-granule attributes became more similar to the model.

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Authors and Affiliations

  1. Polish-Japanese Academy of Information Technology, 02-008, Warsaw, Poland

    Andrzej W. Przybyszewski

  2. Department of Neurology UMass Medical School, Worcester, MA, 01655, USA

    Andrzej W. Przybyszewski

Authors
  1. Andrzej W. Przybyszewski
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Correspondence to Andrzej W. Przybyszewski .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l’Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l’Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l’Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Przybyszewski, A.W. (2019). Parkinson’s Disease Development Prediction by C-Granule Computing. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_24

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  • DOI: https://doi.org/10.1007/978-3-030-28377-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28376-6

  • Online ISBN: 978-3-030-28377-3

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