Parkinson disease prediction using improved crayfish optimization based hybrid deep learning

Article

Malathi, A., Ramalakshmi, R., Gandhi, V. and Bhuvanesh, A. 2024. Parkinson disease prediction using improved crayfish optimization based hybrid deep learning. Technology and Health Care. https://doi.org/10.1177/09287329241296352
TypeArticle
TitleParkinson disease prediction using improved crayfish optimization based hybrid deep learning
AuthorsMalathi, A., Ramalakshmi, R., Gandhi, V. and Bhuvanesh, A.
Abstract

Background
Predicting the course of Parkinson's disease is essential for prompt diagnosis and treatment, which may enhance patient outcomes.
Objective
This study presents a novel method for Parkinson's disease prediction using freely accessible resources. The suggested approach starts with band-pass filter data preprocessing and uses Empirical Mode Decomposition (EMD) for feature extraction. Then, for classification, these features are supplied into an Attention-based Efficient Bidirectional Network (ImCfO_Attn_EffBNet) based on Improved Crayfish Optimization. EfficientNet-B7, BiLSTM, and Attention modules are integrated by ImCfO_Attn_EffBNet to effectively gather temporal and geographic data.
Methods
Additionally, we use the Improved Crayfish Optimization (ImCfO) algorithm to maximize convergence rates, optimize the loss function, and find the global best solutions.
Results
ImCfO enhances performance by adding a self-adaptation criterion to the traditional crayfish algorithm. The classifier's configurable parameters are adjusted using the ImCfO resultant solution, which raises the prediction accuracy overall.
Conclusion
Based on a number of assessments, the ImCfO_Attn_EffBNet analyzed the performance and found that the results were as follows: accuracy (95.068%), recall (92.948%), specificity (92.89%), F-Score (92.89%), precision (92.89%), and FPR (2.1%), in that order.

KeywordsParkinson disease prediction; EfficientNet-B7; Improved Crayfish Optimization; electroencephalography; hybrid classifier; attention mechanism
Sustainable Development Goals3 Good health and well-being
Middlesex University ThemeHealth & Wellbeing
PublisherSAGE Publications
JournalTechnology and Health Care
ISSN0928-7329
Electronic1878-7401
Publication dates
Online20 Nov 2024
Publication process dates
Submitted15 Jun 2024
Accepted29 Sep 2024
Deposited27 Jan 2025
Output statusPublished
Copyright Statement

This is the submitted version of an article published in Technology and Health Care by SAGE Publications.

Digital Object Identifier (DOI)https://doi.org/10.1177/09287329241296352
LanguageEnglish
First submitted version
File Access Level
Open
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