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Performance Analysis of SVM Classification Model for Diagnosis of Alzheimer’s Disease
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 27 |
| Year of Publication: 2021 |
| Authors: Rajasree R.S., S. Brintha Rajakumari, Gajanan Babhulkar, Madhuri Gurale |
10.5120/ijca2021921144
|
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
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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