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 |
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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
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.