Improved diagnosis of Parkinson's disease using optimized crow search algorithm

Diagnosis of Parkinson's disease at its early stage is important in proper treatment of the patients so they can lead productive lives for as long as possible. Although many techniques have been proposed to diagnose the Parkinson's disease at an early stage but none of them are efficient....

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Vydáno v:Computers & electrical engineering Ročník 68; s. 412 - 424
Hlavní autoři: Gupta, Deepak, Sundaram, Shirsh, Khanna, Ashish, Ella Hassanien, Aboul, de Albuquerque, Victor Hugo C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Ltd 01.05.2018
Elsevier BV
Témata:
Algorithms
Chaotic crow search algorithm
Crow search algorithm
Decision Tree
Diagnosis
Feature selection
k-Nearest Neighbor
Medical diagnosis
Optimization
Optimized crow search algorithm
Parkinson's disease
Random Forest
Search algorithms
Chaotic crow search algorithm
Feature selection
Parkinson's disease
Decision Tree
Crow search algorithm
k-Nearest Neighbor
Optimized crow search algorithm
Random Forest
ISSN:0045-7906, 1879-0755
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Shrnutí:Diagnosis of Parkinson's disease at its early stage is important in proper treatment of the patients so they can lead productive lives for as long as possible. Although many techniques have been proposed to diagnose the Parkinson's disease at an early stage but none of them are efficient. In this work, to improve the diagnosis of Parkinson's disease, we have introduced a novel improved and optimized version of crow search algorithm(OCSA). The proposed OCSA can be used in predicting the Parkinson's disease with an accuracy of 100% and help individual to have proper treatment at early stage. The performance of OCSA has been measured for 20 benchmark datasets and the results have been compared with the original chaotic crow search algorithm(CCSA). The experimental result reveals that the proposed nature-inspired algorithm finds an optimal subset of features, maximizing the accuracy and minimizing a number of features selected and is more stable.
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ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2018.04.014