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Intelligent approach of score-based artificial fish swarm algorithm (SAFSA) for Parkinson's disease diagnosis by Syed Haroon Abdul Gafoor; Padma Theagarajan is a Computer Science article available to read on EtoBox.

What is Intelligent approach of score-based artificial fish swarm algorithm (SAFSA) for Parkinson's disease diagnosis about?

## 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 misdiagnosis. Meanwhile, early nonmotor signs of Parkinson’s disease (PD) can be mild and may be due to variety of other conditions. As a result, these signs are usually ignored, making early PD diagnosis difficult. Machine 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 PD. The quantity of the data being processed has grown in the last five years; feature selection has become a prerequisite before any classification. This study introduces a feature selection method based on the score-based artificial fish swarm algorithm (SAFSA) to overcome this issue.

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Author

Syed Haroon Abdul Gafoor; Padma Theagarajan

Publisher

Emerald

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)