Affiliation:
1. Adhiparasakthi Engineering College, India
2. HAL, India
3. King Saud University, Saudi Arabia
Abstract
Neuron degeneration in the human brain causes Parkinson's disease (PD). The technique employed for detecting early PD is based on a parametric analysis of acoustic sounds. Machine learning methods such as SVM, KNN, decision tree, and random forest are used in a significant manner. When compared to machine learning, deep-learning (DL) algorithms always produce greater results. DL algorithms are utilized to assess the patient's voice record. In this work, the authors introduce the MAYFLY optimization algorithm, a nature-inspired metaheuristic optimization technique. The approach utilizes convolutional neural networks (CNNs) coupled with bi-directional gated recurrent units (BiGRUs) to leverage the temporal and spatial features of biomedical data. Experimental evaluations reveal that the proposed approach produces better accuracy of 98.31% and better precision rate (98.78%). These results demonstrate the potential of the MAYFLY-based CNN-BiGRU model in differentiating PD patients from healthy individuals.
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