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Extracting Knowledge from Images of Meanders and Spirals in the Diagnosis of Patients with Parkinson’s Disease

  • SELECTED PAPERS OF THE 8th INTERNATIONAL WORKSHOP “IMAGE MINING. THEORY AND APPLICATIONS”
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Abstract

In this paper, the problem of diagnosing Parkinson’s disease based on handwritten drawings is investigated. Human-drawn spirals and meanders are used as images. A genetic fuzzy classifier is used as a diagnostic tool. This classifier is built using machine learning methods based on a discrete genetic algorithm. The multiobjective non-dominated sorting genetic algorithm was applied in the work. The diagnostic error, the number of terms, and the number of rules were used as objectives. Higher diagnostic accuracy was achieved compared to methods such as naive Bayes classifier, support vector machine and optimum-path forest. In addition, the fuzzy classifier extracts knowledge for the clinician, which makes it possible to understand causal relationships when making a diagnosis. This is achieved thanks to fuzzy rules of the IF-THEN type. These rules use the fuzzy terms “Small”, “Medium”, “Large” to evaluate the values of the features of the image.

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Funding

This research was funded by Ministry of Science and Higher Education of the Russian Federation, project number FEWM-2020–0042 (AAAA-A20–120111190016-9).

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Authors and Affiliations

  1. Tomsk State University of Control Systems and Radioelectronics, 634050, Tomsk, Russia

    K. Sarin, I. Hodashinsky & M. Svetlakov

Authors
  1. K. Sarin
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  2. I. Hodashinsky
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  3. M. Svetlakov
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Corresponding authors

Correspondence to K. Sarin, I. Hodashinsky or M. Svetlakov.

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The authors declare that they have no conflicts of interest.

Additional information

Konstantin Sarin graduated from Tomsk State University of Control Systems and Radioelectronics (TUSUR) in 2002 with a degree in “Automated information processing and control systems”, Associate Professor of the Department for Complex Information Security of Electronic Computing Systems in TUSUR. Research interests: fuzzy systems, data analysis, machine learning, and biometrics.

Ilya Hodashinsky was born in 1953, graduated from the Faculty of Control Systems, Novosibirsk Electrotechnical Institute (NETI) in 1975. Received the Cand. Sci. degree in 1984, the D.Sc. degree in 2004 from the TUSUR, Russia. Received the Professor title at the 2011. He is a Professor of TUSUR. His main research interests include the computational intelligence, fuzzy modeling, pattern recognition, knowledge discovery, and data mining. He is author and co-author of over 150 journal and conference papers as well as technical articles. Prof. Hodashinsky is a member of IEEE, IEEE Computational Intelligence Society.

Mikhail Svetlakov graduated from TUSUR in 2019 with a degree in 10.05.04 “Information and analytical security systems”, postgraduate student of TUSUR (09.06.01 “Informatics and Computer Engineering”), Junior Researcher employee of LSAUBS (TUSUR), Lecturer at the Faculty of Security (TUSUR). Research interests: computational intelligence, metaheuristic optimization methods, deep learning methods, clustering, fuzzy systems, and biometrics.

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Sarin, K., Hodashinsky, I. & Svetlakov, M. Extracting Knowledge from Images of Meanders and Spirals in the Diagnosis of Patients with Parkinson’s Disease. Pattern Recognit. Image Anal. 32, 658–664 (2022). https://doi.org/10.1134/S1054661822030385

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