Alzheimer Detection based on Optimized Machine Learning for EEG Signals

Alzheimer's disease is a prevalent nervous system illness among the elderly population, and its likelihood of developing rises with age. One potential non-invasive measurement method that is frequently used to gauge changes in brain impulses is the electroencephalogram. To differentiate Alzheimer's patients, EEG waves are analyzed. Several studies assessed the clinical significance of Alzheimer's disease. In particular, recently developed evaluation techniques based on machine learning bring the fact to light by extracting features from EEG signals. Optimizing the network parameters of machine learning models using optimization algorithms can also lead to enhanced performance. Consequently, this study effort presents an optimized machine learning model for deep belief network-based Alzheimer's disease identification.. Particle Swarm Optimization is included in the proposed work to optimize the deep belief network parameters. The results are compared with the traditional deep belief network and illustrate that the suggested method outperforms the traditional approach.