{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:14:22Z","timestamp":1783296862540,"version":"3.54.6"},"reference-count":28,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multiple Sclerosis (MS) is a central nervous system disease that causes ataxia and balance disorders. In ataxia, the first symptom is usually seen as gait disturbance. In gait ataxia, symptoms can be clinically defined by shortened stride length and irregular strides. Evaluation of gait disturbance in clinical cases is important for the detection of the first stage of ataxia. With the increasing amount of data, high-performance models can be produced, especially in the field of healthcare, with computer machine learning, deep learning and artificial intelligence methods. This study aimed to identify ataxia in individuals with Multiple Sclerosis (MS) by analysing images that encompass plantar pressure distribution signals. A total of 105 images, each containing plantar pressure distribution signals, were utilized to extract features through pre-trained EfficientNet architectures. Then the feature vectors obtained were classified by SVM, k-NN, and ANN methods. As a result of this study, the best classification performance was obtained with SVM classifier with 88.09 % Acc, 80.55 % Sen, 93.75 % Spe and 85.29 % F1 Score. The results show that the study will help the clinician in the detection of PwMS ataxia and will be a pioneer for future studies.<\/jats:p>","DOI":"10.2478\/acss-2024-0006","type":"journal-article","created":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T03:26:29Z","timestamp":1723692389000},"page":"45-52","source":"Crossref","is-referenced-by-count":2,"title":["Determination of Ataxia with EfficientNet Models in Person with Early MS using Plantar Pressure Distribution Signals"],"prefix":"10.2478","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0526-4526","authenticated-orcid":false,"given":"Taner","family":"Tuncer","sequence":"first","affiliation":[{"name":"Firat University , El\u00e2z\u0131\u011f , Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asl\u0131","family":"Sesli","sequence":"additional","affiliation":[{"name":"Firat University , El\u00e2z\u0131\u011f , Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6472-8306","authenticated-orcid":false,"given":"Seda Arslan","family":"Tuncer","sequence":"additional","affiliation":[{"name":"Firat University , El\u00e2z\u0131\u011f , Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"2026070522563803691_j_acss-2024-0006_ref_001","doi-asserted-by":"crossref","unstructured":"W. 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Bilek, \u201cPlantar Bas\u0131n\u00e7 Da\u011f\u0131l\u0131m\u0131 Sinyalleri Kullan\u0131larak Erken MSlilerde Ataksinin Hybrt CNN Modelleri ile Belirlenmesi,\u201d Avrupa Bilim ve Teknoloji Dergisi, Ejosat Special Issue 2021 (ICAENS), pp. 579\u2013583, 2021.","DOI":"10.31590\/ejosat.1009129"},{"key":"2026070522563803691_j_acss-2024-0006_ref_025","doi-asserted-by":"crossref","unstructured":"A., Marquer, G. Barbieri, and D. P\u00e9rennou, \u201cThe assessment and treatment of postural disorders in cerebellar ataxia: a systematic review,\u201d Annals of Physical and Rehabilitation Medicine, vol. 57, no. 2, pp. 67\u2013 78, Mar. 2014. https:\/\/doi.org\/10.1016\/j.rehab.2014.01.002","DOI":"10.1016\/j.rehab.2014.01.002"},{"key":"2026070522563803691_j_acss-2024-0006_ref_026","unstructured":"M. Tan and Q. Le, \u201cEfficientNet: Rethinking model scaling for convolutional neural networks,\u201d in International Conference on Machine Learning, vol. 97, 2019, pp. 6105\u20136114. https:\/\/proceedings.mlr.press\/v97\/tan19a.html"},{"key":"2026070522563803691_j_acss-2024-0006_ref_027","doi-asserted-by":"crossref","unstructured":"V. Vapnik, \u201cThe support vector method of function estimation,\u201d in Nonlinear Modeling, Springer, 1998, pp. 55\u201385. https:\/\/doi.org\/10.1007\/978-1-4615-5703-6_3","DOI":"10.1007\/978-1-4615-5703-6_3"},{"key":"2026070522563803691_j_acss-2024-0006_ref_028","doi-asserted-by":"crossref","unstructured":"C. He, M. Ma, and P. 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