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Classification of Alzheimer’s disease based on brain MRI and machine learning

  • Zhao FanEmail author
  • Fanyu Xu
  • Xuedan Qi
  • Cai Li
  • Lili Yao
Deep Learning & Neural Computing for Intelligent Sensing and Control
  • 11 Downloads

Abstract

Alzheimer’s disease (AD) is one of the most common diseases in the world. It is a neurodegenerative disease that can cause cognitive impairment and memory deterioration. In recent years, the number of the elderly population is increasing, and the incidence of elderly diseases has increased significantly. The most representative of these diseases is Alzheimer’s disease. According to some data, the average survival time of Alzheimer’s disease patients is only 5.5 years, which is the “fourth killer” that endangers the health of the elderly after cardiovascular diseases, cerebrovascular diseases and cancer. According to conservative estimates of the International Federation of Alzheimer’s Diseases, the number of Alzheimer’s disease patients worldwide will increase to 75.62 million by 2030; by 2050, the number of patients will reach 135.46 million. Therefore, it is urgent to classify the course of Alzheimer’s disease. In this paper, support vector machine (SVM) model method is used to classify and predict different disease processes of Alzheimer’s disease based on structural brain magnetic resonance imaging (MRI) imaging data, so as to help the auxiliary diagnosis of the disease. In this paper, the extracted MRI data and the SVM model are combined to obtain more accurate classification prediction results. The accuracy of classification and prediction is the best. According to the predicted results, the data characteristics related to diseases can be determined, which can provide a basis for clinical and basic research, etiology and pathological changes.

Keywords

Machine learning Support vector machine Brain MRI Alzheimer’s disease 

Notes

Acknowledgements

This work was supported by: (1) Studying Abroad Scholarships by Department of Resource and Social Security of Shanxi Province (Grant/Award: 619017); (2) Shanxi scholarship council of China (Grant/Award No. 2016-061); (3) International Cooperation Project, the Shanxi Science and Technology Department (Grant/Award No. 201803D421068).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Zhao Fan
    • 1
    Email author
  • Fanyu Xu
    • 2
  • Xuedan Qi
    • 3
  • Cai Li
    • 4
  • Lili Yao
    • 4
  1. 1.Institute of GeriatricsShanxi Medical UniversityTaiyuanChina
  2. 2.School of StomatologyShanxi Medical UniversityTaiyuanChina
  3. 3.306 Hospital of PLABeijingChina
  4. 4.Department of PhysiologyBasic Medical Sciences of Shanxi Medical UniversityTaiyuanChina

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