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Open AccessArticle
Deep Learning for Parkinson’s Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein–Peptide Datasets
by
Prabhavathy Mohanraj
Prabhavathy Mohanraj 1,*,
Valliappan Raman
Valliappan Raman 1 and
Saveeth Ramanathan
Saveeth Ramanathan 2
1
Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore 641014, India
2
Department of Computer Science and Engineering, Coimbatore Institute of Technology, Coimbatore 641014, India
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(19), 2181; https://doi.org/10.3390/diagnostics14192181 (registering DOI)
Submission received: 6 August 2024
/
Revised: 25 August 2024
/
Accepted: 19 September 2024
/
Published: 29 September 2024
Abstract
Abstract: Background: An important neurological disorder of Parkinson’s Disease (PD) is characterized by motor and non-motor activity of the patients. Empirical condition of the patient: PD assessment uses the Movement Disorder Society Unified Parkinson’s Rating Scale part III (MDS-UPDRS-III) measures for identifying the prediction of PD. Due to the unstable value of the measurement, the PD prediction and tracking lead to a lower prediction rate. Methods: To overcome this limitation, this paper proposed the Graph Wavelet Transform (GWT) based weighted feature extraction along with the Graph Neutral Network (GNN) classification. The main contribution of this research is (i) The weighted correlation between the data is calculated by GWT for effective prediction of PD. (ii) Machine learning algorithms were trained to predict Parkinson’s disease based on these patterns. In this research, we developed a new model called Graph Neural Network (GNN) to predict PD tremors’ MDS-UPDRS-III score using input data. To strengthen PD research and enable the construction of individualized treatment plans, these linked networks work together to methodically examine the data and find significant discoveries. Results: The proposed approach for predicting PD severity (motor- and MDS_UPDRS) has a mean squared error of 0.1796 and a root mean squared error of 0.2845, according to the experimental data. The prediction accuracy is increased by 27.66%, 54.11%, and 0.71%, correspondingly, when compared with the most effective State-of-the-Art methods of DNN, ANFIS + SVR, and Mixed MLP models. Conclusion: In conclusion, this proves that the proposed strategy is more effective at making predictions.
Share and Cite
MDPI and ACS Style
Mohanraj, P.; Raman, V.; Ramanathan, S.
Deep Learning for Parkinson’s Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein–Peptide Datasets. Diagnostics 2024, 14, 2181.
https://doi.org/10.3390/diagnostics14192181
AMA Style
Mohanraj P, Raman V, Ramanathan S.
Deep Learning for Parkinson’s Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein–Peptide Datasets. Diagnostics. 2024; 14(19):2181.
https://doi.org/10.3390/diagnostics14192181
Chicago/Turabian Style
Mohanraj, Prabhavathy, Valliappan Raman, and Saveeth Ramanathan.
2024. "Deep Learning for Parkinson’s Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein–Peptide Datasets" Diagnostics 14, no. 19: 2181.
https://doi.org/10.3390/diagnostics14192181
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