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However, existing methods are lacking in accurately recognizing the Parkinson\u2019s disease and suffer from efficiency problems. To overcome these problems faced by existing models, this paper presents a machine-learning-based model for Parkinson\u2019s disease recognition. Specifically, a hybrid feature selection algorithm has been designed by integrating the Relief and ant-colony optimization algorithms to select relevant features for training the model. Moreover, the support vector machine has been trained and tested on the selected features to achieve optimal classification accuracy. Additionally, the K-fold cross-validation technique has been employed for the optimal hyper-parameters value evaluation of the model.The experimental results on a real-world dataset, i.e., Parkinson\u2019s disease dataset is revealed that the proposed system outperforms baseline competitors by accurately recognizing the Parkinson\u2019s disease and achieving 99.50% accuracy on the selected features. Due to high performance is achieved our proposed method, we are highly recommended for the recognition of PD.<\/jats:p>","DOI":"10.3233\/jifs-200075","type":"journal-article","created":{"date-parts":[[2020,5,15]],"date-time":"2020-05-15T12:51:01Z","timestamp":1589547061000},"page":"1319-1339","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":22,"title":["Recognition of the parkinson\u2019s disease using a hybrid feature selection approach"],"prefix":"10.1177","volume":"39","author":[{"given":"Amin","family":"Ul Haq","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Hammad","family":"Memon","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jalaluddin","family":"khan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zafar","family":"Ali","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Zaheer","family":"Abbas","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shah","family":"Nazir","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Swabi, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,5,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.08.022"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.7326\/M15-2256"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2003.821450"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/1878-5085-3-16"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"BonabeauE. 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