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• W4251010209 abstract "Parkinson's disease (PD) is a highly common neurological disease affecting a large population worldwide. Several studies reveal that the degradation of voice is one of its initial symptoms, which is also known as dysarthria. This work explores the correlation between various features in the voice samples observed in PD subjects, using which a novel two-level ensemble-based feature selection method has been proposed. This selected feature set is fed to a custom fine-tuned multilayer perceptron (MLP)-based classifier while using k-fold cross-validation as the resampling strategy. For a robust model training and analysis, three separate benchmark datasets of voice samples were used for the experimentation. Several performance metrics including accuracy, precision, area under the curve (AUC), Matthew's correlation coefficient (MCC), and F1-score were evaluated from the trained model. Results strongly suggest that the proposed feature selection framework helps in identifying an optimal set of features which greatly assist the MLP classifier for a highly accurate identification of PD patients. The proposed model achieves an overall accuracy of 98.3%, 95.1%, and 100% on the three selected datasets. These results are significantly better than those achieved by the non-feature selection-based studies, and even the recently proposed chi-square based feature selection option." @default.
• W4251010209 created "2022-05-12" @default.
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• W4251010209 date "2021-11-01" @default.
• W4251010209 modified "2023-10-16" @default.
• W4251010209 title "An ensemble‐based feature selection framework for early detection of Parkinson's disease based on feature correlation analysis" @default.
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• W4251010209 doi "https://doi.org/10.1002/mma.7835" @default.
• W4251010209 hasPublicationYear "2021" @default.
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