Classification and diagnosis of Alzheimer's disease based on multimodal data

Conference: ICETIS 2022 - 7th International Conference on Electronic Technology and Information Science
01/21/2022 - 01/23/2022 at Harbin, China

Proceedings: ICETIS 2022

Pages: 5Language: englishTyp: PDF

Authors:
Zhu, Bing; Li, Qi (School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China)
Xi, Yang (School of Computer Science, Northeast Electric Power University, Jilin, China)
Guo, Chunjie (Department of Radiology, The First Hospital of Jilin University, Changchun, China)
Yang, Yu (Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China)
Wu, Jinglong; Zhang, Zhilin (Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China)

Abstract:
Alzheimer's disease is an irreversible neurodegenerative disease, and exploring early diagnostic methods can benefit patients in obtaining accurate and effective treatment. This study adopted multimodal data of clinical neuropsychological examinations and functional Magnetic Resonance Imaging brain network properties constructed by graph theory. Scales, global brain network properties and local properties with significant differences were used as features in patients with Alzheimer's disease, patients with mild cognitive impairment, and normal elderly people. The feature significances were analyzed, and three features and feature combinations calculated using Support Vector Machine and Naive Bayes Classifiers were compared. The results indicated that the scales and local brain network properties had better classification effects in the diagnosis, and the trichotomous classification accuracy of the two classifiers for all feature combinations was 85.07% and 88.06%, respectively. The feature selection method proposed in this paper has an auxiliary effect on the classification diagnosis.