Abstract
In this paper, a method for diagnosing Parkinson’s disease based on features derived from hand-drawn spirals is presented. During drawing of these spirals on a tablet, coordinates of points of the spiral, pressure and angle of the pen at that point, and timestamp were registered. A set of features derived from the registered data, by means of which the classification was performed, has been proposed. For testing purposes, classification has been done by means of several of the most popular machine learning methods, for which the accuracy of Parkinson’s disease recognition was determined. The study has proven that the proposed set of features enables the effective diagnosis of Parkinson’s disease. The proposed method can be used in screening tests for Parkinson’s disease.
The experiments were conducted on a publicly available “Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet Data Set” database from the UCI archives. This database contains drawings of spirals made by people with Parkinson’s disease as well as by healthy people.
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Wrobel, K., Doroz, R., Porwik, P., Orczyk, T., Cavalcante, A.B., Grajzer, M. (2022). Features of Hand-Drawn Spirals for Recognition of Parkinson’s Disease. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_37
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