Open AccessAlzheimer Disease Classification using Machine Learning
Author(s) -
Ghazala Begum,
A Sai Manisha
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2942.049620
Subject(s) - support vector machine , margin (machine learning) , obstacle , random forest , dementia , artificial intelligence , computer science , machine learning , disease , set (abstract data type) , psychology , medicine , geography , archaeology , pathology , programming language
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 [1]. 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”,