This paper is retracted in Volume-8, Issue-3, 2022
Area
Computer Science
Author
Tanvi Mahajan, Dr. Jyoti Srivastava
Org/Univ
National Institute of Technology, Hamirpur, Himachal Pradesh, India
Sub. Date
23 May, 2022
Paper ID
V8I3-1338
Publisher
Keywords
Dementia, Alzheimer Disease, MRI, Machine learning, Deep L earning, Classification

Abstract

Dementia has developed into a significant health problem for many over the age of 50. Numerous kinds of dementia develop gradually and incrementally. Previous researchers have received reports from persons of various ages who have had memory loss and subsequent recall from long-term memory loss as a result of this neurodegenerative illness. Memory loss that is both gradual and irreversible characterises the disorder known as dementia. Although it is more common in the elderly, an increase in cases among the younger population has raised experts' eyes and encouraged them to explore the neuro-disorder, which causes memory loss and a barrier in recalling information from memory. Dementia can slow down to some extent if diagnosed early enough. Optimize learning and an additional tree classifier are used to extract information from brain MRI images and classify dementia at an early stage. In order to discover various patterns of dementia risk, hyper-parameters resulting from XGboost were obtained and assessed. Gradient boosting is commonly used to do variable extractions from independent to dependent variables, and the resulting derived variables are the result of this process