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A Review of Prediction on Alzheimer's Disease Using Machine Learning Techniques
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Author(s): A. Praveena (Vels Institute of Science, Technology, and Advanced Studies, India)and M. Yogeshwari (Vels Institute of Science, Technology, and Advanced Studies, India)
Copyright: 2024
Pages: 13
Source title:
Advancements in Clinical Medicine
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5946-4.ch026
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Abstract
Alzheimer's disease is the most typical neurological disorder. There are about 45 million people who have this illness. Alzheimer's disease (AD) is a neurodegenerative disorder that impacts neurons, brain cells, and neurotransmitters and affects perception, memory, and behavior. Even though the symptoms are mild at first, they always worsen. There is currently no cure for AD. However, taking recommended medications can slow the spread of the illness. Early Alzheimer's diagnosis is, therefore, crucial for both therapy and future research. The main challenges in early AD identification using various classification algorithms are the extremely low numbers of trained samples and greater feature descriptions. The disease rendered sufferers' incapable of thinking, reading, and doing a wide range of other tasks. By anticipating the disease, a machine learning system may be able to lessen this issue. Finding dementia in a range of persons is the main objective.
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