Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics

Parkinson’s disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and...

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Veröffentlicht in:Scientific Reports Jg. 14; H. 1; S. 4309 - 12
Hauptverfasser: Cuk, Aleksa, Bezdan, Timea, Jovanovic, Luka, Antonijevic, Milos, Stankovic, Milos, Simic, Vladimir, Zivkovic, Miodrag, Bacanin, Nebojsa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer Science and Business Media LLC 21.02.2024
Nature Publishing Group UK
Nature Publishing Group
Nature Portfolio
Schlagworte:
631/114/1305
631/114/2164
631/114/2397
631/114/2398
Basal Ganglia
Crayfish optimization algorithm
Disease detection
Dopamine receptors
Gait
Humanities and Social Sciences
Humans
Long-short term memory neural networks
Medical diagnosis
Medicine
Movement disorders
multidisciplinary
Neural networks
Neural Networks, Computer
Neurodegenerative diseases
Optimization
Parkinson Disease - diagnosis
Parkinson's disease
Q
Quality of life
R
Rigidity
Science
Science (multidisciplinary)
Short term memory
Medical diagnosis
Long-short term memory neural networks
Crayfish optimization algorithm
Parkinson’s disease
Optimization
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Parkinson’s disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson’s disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson’s disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-54680-y