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Submitted: 05 Jul 2020
Accepted: 26 Aug 2020
ePublished: 30 Sep 2020
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Int Electron J Med. 2020;9(3): 116-120.
doi: 10.34172/iejm.2020.21
  Abstract View: 72
  PDF Download: 39

Original Article

Prediction of Alzheimer’s Disease Using Machine Learning Classifiers

Mansour Rezaei 1 ORCID logo, Ehsan Zereshki 2* ORCID logo, Soodeh Shahsavari 3 ORCID logo, Mohammad Gharib Salehi 4 ORCID logo, Hamid Sharini 5 ORCID logo

1 Department of Biostatistics, Kermanshah University of Medical Sciences, Kermanshah, Iran.
2 Student Research Committee, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
3 Department of Health Information Management, School of Allied Medical Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran.
4 Department of Radiology, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
5 Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
*Correspondence to Ehsan Zereshki, Student Research Committee, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran. Tel: +989394416710, Email: ehsanzereshki@ gmail.com

Abstract

Background: Alzheimer’s disease (AD) is the most common brain failure for which no cure has yet been found. The disease starts with a disturbance in the brain structure and then it manifests itself clinically. Therefore, by timely and correct diagnosis of changes in the structure of the brain, the occurrence of this disease or at least its progression can be prevented. Due to the fact that magnetic resonance imaging (MRI) can be used to obtain very useful information from the brain, and also because it is non-invasive, this method has been considered by researchers.

Materials and Methods: The data were obtained from an MRI database (MIRIAD) of 69 subjects including 46 AD patients and 23 healthy controls (HC). Individuals were categorized based on two criteria including NINCDS-ADRAD and MMSE, as the gold standard. In this paper, we used the support vector machine (SVM) and Bayesian SVM classifiers.

Results: Using the SVM classifier with Gaussian radial basis function (RBF) kernel, we distinguished AD and HC with an accuracy of 88.34%. The most important regions of interest (ROIs) in this study included right para hippocampal gyrus, left para hippocampal gyrus, right hippocampus, and left hippocampus.

Conclusion: This study showed that the SVM model with Gaussian RBF kernel can distinguish AD from HC with high accuracy. These studies are of great importance in medical science. Based on the results of this study, MRI centers and neurologists can perform AD screening tests in people over the age of 50 years.

Keywords: Alzheimer's disease, Support vector machine, Machine learning, Magnetic resonance imaging
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