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Intelligent approach of score-based artificial fish swarm algorithm(SAFSA)for Parkinson’s disease diagnosis

International Journal of Intelligent Computing and Cybernetics 2022年第4期15卷 540-561页

作者： Syed Haroon Abdul Gafoor padma theagarajan Bharathiar University CoimbatoreIndia Department of Computer Applications Sona College of TechnologySalemIndia

Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in ***,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other *** a result,these signs are usually ignored,making early PD diagnosis *** learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose *** quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any *** study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this ***-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible *** subset selection in PD detection techniques starts by eliminating features that are not relevant or *** to a few objective functions,features subset chosen should provide the best *** limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)*** method enhances the PD detection rate by selecting the most essential features from the *** begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality ***,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for find

关键词： Parkinson disease dysphonia features Feature subset selection Score-based artificial fish swarm algorithm(SAFSA) Singular value decomposition(SVD) Classification

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