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Current Neuroinnovative Techniques With Machine Learning Algorithms in the Diagnosis and Classification of Neurodegenerative Diseases
Abstract
Neurodegenerative diseases are one of the most debilitating diseases associated with progressive neuronal dysfunction and protein accumulation. The increasing prevalence of Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) in population is leading to the development of neuroinnovative techniques for early recognition, classification and treatment. Various computer-aided software programs based on machine learning (ML) studies have been developed in recent decades in medical scope. ML is a branch of artificial intelligence that recognizes and models different data sets and algorithms. Different disciplines such as neural networks, support vector machines, Decision Trees, Random Forests, logistic regression, k-Nearest Neighbor techniques such as multi-layer perceptron, Neural Networks, Gaussian Mixture Models, and Boosting methods have been introduced in neurology. This section will discuss neuropathological and neuroradiological evaluation of AD, PD, and ALS, and ML-based modeling algorithms used in diagnosing and classification so far.
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