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• W4298042874 abstract "According to the World Health Organization, neurodegenerative diseases such as Huntington's disease, Parkinson's disease, progressive brain dysfunction, vascular dementia, cognitive impairment, Alzheimer's disease, and Amyotrophic Lateral Sclerosis account for more than seven million deaths and morbidity of one billion people worldwide, making research on a variety of neurodegenerative diseases particularly important. Several improvements in neuroimaging data acquisition approaches such as diffusion Magnetic Resonance and data mining from electroencephalogram are channeled toward classification of different neurodegenerative diseases and treatment options. Though, several challenges are encountered in the proper diagnosis, analysis, and classification of these neurodegenerative disorders such as increased dimensionality, nonlinearity, and nonstationarity. As a result, various machine learning approaches and algorithms such as reinforcement learning, semisupervised learning, unsupervised learning, supervised learning, deep learning, decision tree, BF tree, Bagging, Random Forest tree, RBF networks, and evolutionary learning are required to accurately classify and treat many of these neurodegenerative diseases. Several gait biomarkers, postural disorders, memory disorders, could be employed as quantitative assessment in early diagnosis and treatment strategies utilizing computer assisted diagnostic systems. Therefore, this chapter will focus on recent advances in the classification of neurodegenerative diseases utilizing machine learning algorithms." @default.
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• W4298042874 date "2023-01-01" @default.
• W4298042874 modified "2023-10-16" @default.
• W4298042874 title "Classification of neurodegenerative disorders using machine learning techniques" @default.
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• W4298042874 doi "https://doi.org/10.1016/b978-0-323-90277-9.00028-6" @default.
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