Parkinson's Disease Prediction Using Wave Frequency Data with Enhanced Graph Neural Networks
Keywords:
Biased Binary Bat, ElasticNet, Graph Neural Network, Improved Biased K-Means Clustering, Parkinson's diseaseAbstract
With disproportionately high rates among the elderly, Parkinson's disease (PD) ranks as the world's second most common degenerative neurological ailment. For better treatment outcomes, early detection of PD is crucial, as the majority of symptoms do not manifest until the illness has progressed significantly. In this research we propose parkinson's disease prediction using wave frequency data with enhanced graph neural networks (E-GNN). The dataset was collected from Kaggle repository. After collecting the dataset we use Improved Biased K-Means Clustering algorithm for preprocessing. After preprocessing we use Biased Binary Bat with ElasticNet algorithm for feature selection. After feature selection we use Enhanced Graph Neural Network algorithm for parkinson's disease classification. This E-GNN incorporates advanced enhancements in both architecture and training methodology, pushing the boundaries of accuracy in predicting Parkinson's disease. The integration of cutting-edge techniques within the E-GNN framework enables the model to capture intricate relationships and dependencies within the data, ultimately resulting in more precise and reliable predictions.
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