ArticleParkinson's disease2019
Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson's Disease in LabVIEW Environment.
Article in Parkinson's disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neuroinflammation as a Link in Parkinson's and Alzheimer's Diseases: A Systematic Review and Meta-Analysis.Aging and disease · 2024Pooled it
- CNN-Based Identification of Parkinson's Disease from Continuous Speech in Noisy Environments.Bioengineering (Basel, Switzerland) · 2023Article
- Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson's Disease.Frontiers in molecular neuroscience · 2022Article
- Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis.BioMed research international · 2019Article
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Authors and funding
1 author.
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Abstract
Optimization is the process of achieving the best solution for a problem. LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method. PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples. Also, SVM parameters are optimized for Parkinson's disease data by combining CS and PSO. The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better. The hybrid model shows that the accuracy of the performance achieved is 97.4359%. Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall,
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