Affiliation:
1. Adhiparasakthi Engineering College, India
2. HAL, India
3. King Saud University, Saudi Arabia
Abstract
Machine learning is an evolving technology, which uses deep learning, a subset of artificial intelligence, harnesses neural networks, mirroring the human brain, for feature extraction and manipulation. Models like CNN, RNN, LSTM, SOM, among others, expedite data interpretation from vast datasets. Parkinson's disease, a neurodegenerative ailment primarily impacting dopamine-producing neurons within the substantia nigra of the brain, lacks a known cause and cure. Deep learning models play a pivotal role in early Parkinson's detection. In this study, a convolutional bi-directional GRU approach is employed to identify Parkinson's disease, with MAYFLY optimization for feature selection. Utilizing handwriting samples from Parkinson's patients, the proposed algorithm achieves a remarkable 96.40% accuracy in predicting the disease, facilitating early treatment interventions for affected individuals.
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