کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6853677 | 1437241 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimized cuttlefish algorithm for diagnosis of Parkinson's disease
ترجمه فارسی عنوان
الگوریتم بهینه سازی کتلفی برای تشخیص بیماری پارکینسون
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کلمات کلیدی
بیماری پارکینسون، بهینه سازی الگوریتم تربچه، استخراج ویژگی، فراگیری ماشین، الگوریتمهای تکاملی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper presents an optimized cuttlefish algorithm for feature selection based on the traditional cuttlefish algorithm, which can be used for diagnosis of Parkinson's disease at its early stage. Parkinson is a central nervous system disorder, caused due to the loss of brain cells. Parkinson's disease is incurable and could eventually lead to death but medications can help to control symptoms and elongate the patient's life to some extent. The proposed model uses the traditional cuttlefish algorithm as a search strategy to ascertain the optimal subset of features. The decision tree and k-nearest neighbor classifier as a judgment on the selected features. The Parkinson speech with multiple types of sound recordings and Parkinson Handwriting sample's datasets are used to evaluate the proposed model. The proposed algorithm can be used in predicting the Parkinson's disease with an accuracy of approximately 94% and help individual to have proper treatment at early stage. The experimental result reveals that the proposed bio-inspired algorithm finds an optimal subset of features, maximizing the accuracy, minimizing number of features selected and is more stable.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Cognitive Systems Research - Volume 52, December 2018, Pages 36-48
Journal: Cognitive Systems Research - Volume 52, December 2018, Pages 36-48
نویسندگان
Deepak Gupta, Arnav Julka, Sanchit Jain, Tushar Aggarwal, Ashish Khanna, N. Arunkumar, Victor Hugo C. de Albuquerque,