Research Article
Performance Analysis of SVM Classification Model for Diagnosis of Alzheimer’s Disease
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 174 - Issue 27 |
| Published: Mar 2021 |
| Authors: Rajasree R.S., S. Brintha Rajakumari, Gajanan Babhulkar, Madhuri Gurale |
10.5120/ijca2021921144
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Rajasree R.S., S. Brintha Rajakumari, Gajanan Babhulkar, Madhuri Gurale . Performance Analysis of SVM Classification Model for Diagnosis of Alzheimer’s Disease. International Journal of Computer Applications. 174, 27 (Mar 2021), 37-40. DOI=10.5120/ijca2021921144
@article{ 10.5120/ijca2021921144,
author = { Rajasree R.S.,S. Brintha Rajakumari,Gajanan Babhulkar,Madhuri Gurale },
title = { Performance Analysis of SVM Classification Model for Diagnosis of Alzheimer’s Disease },
journal = { International Journal of Computer Applications },
year = { 2021 },
volume = { 174 },
number = { 27 },
pages = { 37-40 },
doi = { 10.5120/ijca2021921144 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2021
%A Rajasree R.S.
%A S. Brintha Rajakumari
%A Gajanan Babhulkar
%A Madhuri Gurale
%T Performance Analysis of SVM Classification Model for Diagnosis of Alzheimer’s Disease%T
%J International Journal of Computer Applications
%V 174
%N 27
%P 37-40
%R 10.5120/ijca2021921144
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Alzheimer’s disease (AD) is a type of Dementia which affects the brain and causes memory loss. It disrupts a person’s ability to function independently. In this paper we have considered some measures such as Age, MMSE scores, whole brain volume and endocrinal volume. In our work, we have proposed a classification model using SVM model and anlaysed the performance of SVM model for different kernel methods. Moreover a five fold cross validation approach is used to improve the performance oof the model. The results shows that linear and polynomial kernel methods give a classification accuracy of 73.2% and AUC of 0.7.
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