ArticleScientific reports2024
Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 48 citations in OpenAlex.
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- Simulated depression risk classification from Parkinson's voice features using a self-attention-enhanced MLP architecture.Scientific reports · 2026Article
- Computational Approaches to Neurological Disorder Diagnosis: An In-Depth Review of Current Methods and Future ProspectsCurrent medical imaging · 2026Review
- Automated detection of Parkinson's disease using improved linknet-ghostnet model based on handwriting images.Scientific reports · 2025Article
- Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images.Scientific reports · 2025Article
- Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy.PloS one · 2025Article
- A novel feature extraction method based on dynamic handwriting for Parkinson's disease detection.PloS one · 2025Article
- Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles.Scientific reports · 2024Article
- Late feature fusion using neural network with voting classifier for Parkinson's disease detection.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 4 countries.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.