Alzheimer Disease Classification using Machine Learning
Gousiya Begum1, A. Manisha2
1Gousiya Begum, CSE Department, Mahatma Gandhi Institute of Technology, Hyderabad, India.
2A Sai Manisha, CSE Department, Mahatma Gandhi Institute of Technology, Hyderabad, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 27, 2020. | Manuscript published on April 10, 2020. | PP: 6-9 | Volume-9 Issue-6, April 2020. | Retrieval Number: E2942039520/2020©BEIESP | DOI: 10.35940/ijitee.E2942.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Alzheimer’s disease is the most popular and persuading dementia that affects our memory power, reasoning and deportment. Symptoms rise up slowly and worsen with time, becoming an obstacle in doing our routine tasks. Alzheimer is not conventional wedge of aging. The substantial and known risk factor is up surging age. The prevalence of AD is depicted to be around 5% after an age of 65 years and took a leap of 30% for people of 85 years old in developed countries . In this project we proposed a detection and classification technique using Random Forest(RF) and Support Vector Machine(SVM) algorithms on the oasis longitudinal data set and compare their respective accuracies to come to a conclusion that which algorithm best suits for this detection and classification. Paper Setup must be in A4 size with Margin: Top 0.7”,
Keywords: Alzheimer, Random Forest, SVM
Scope of the Article: Machine Learning