Identification of Conversion from Normal Elderly Cognition to Alzheimer’s Disease using Multimodal Support Vector Machine

Ye Zhan; Kewei Chen; Xia Wu; Daoqiang Zhang; Jiacai Zhang; Li Yao; Xiaojuan Guo; Alzheimer’s Disease Neuroimaging Initiative

doi:10.3233/JAD-142820

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Identification of Conversion from Normal Elderly Cognition to Alzheimer’s Disease using Multimodal Support Vector Machine

Journal article

Peer reviewed

Identification of Conversion from Normal Elderly Cognition to Alzheimer’s Disease using Multimodal Support Vector Machine

Ye Zhan, Kewei Chen, Xia Wu, Daoqiang Zhang, Jiacai Zhang, Li Yao, Xiaojuan Guo and Alzheimer’s Disease Neuroimaging Initiative

Journal of Alzheimer's disease, Vol.47(4), pp.1057-1067

2015

DOI: 10.3233/JAD-142820

PMCID: PMC6287610

PMID: 26401783

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https://www.ncbi.nlm.nih.gov/pmc/articles/6287610View

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Abstract

Alzheimer’s disease (AD) is one of the most serious progressive neurodegenerative diseases among the elderly, therefore the identification of conversion to AD at the earlier stage has become a crucial issue. In this study, we applied multimodal support vector machine to identify the conversion from normal elderly cognition to mild cognitive impairment (MCI) or AD based on magnetic resonance imaging and positron emission tomography data. The participants included two independent cohorts (Training set: 121 AD patients and 120 normal controls (NC); Testing set: 20 NC converters and 20 NC non-converters) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The multimodal results showed that the accuracy, sensitivity, and specificity of the classification between NC converters and NC non-converters were 67.5%, 73.33%, and 64%, respectively. Furthermore, the classification results with feature selection increased to 70% accuracy, 75% sensitivity, and 66.67% specificity. The classification results using multimodal data are markedly superior to that using a single modality when we identified the conversion from NC to MCI or AD. The model built in this study of identifying the risk of normal elderly converting to MCI or AD will be helpful in clinical diagnosis and pathological research.

Magnetic Resonance Imaging

support vector machine

Alzheimer’s disease

classification

normal elderly

positron emission tomography

Details

Title: Subtitle: Identification of Conversion from Normal Elderly Cognition to Alzheimer’s Disease using Multimodal Support Vector Machine
Creators: Ye Zhan - College of Information Science and Technology, Beijing Normal University, Beijing, China
Kewei Chen - Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, Arizona, USA
Xia Wu - College of Information Science and Technology, Beijing Normal University, Beijing, China
Daoqiang Zhang - Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Jiacai Zhang - College of Information Science and Technology, Beijing Normal University, Beijing, China
Li Yao - College of Information Science and Technology, Beijing Normal University, Beijing, China
Xiaojuan Guo - College of Information Science and Technology, Beijing Normal University, Beijing, China
Alzheimer’s Disease Neuroimaging Initiative
Contributors: Laura L Boles-Ponto (Contributor) - University of Iowa, Radiology
Resource Type: Journal article
Publication Details: Journal of Alzheimer's disease, Vol.47(4), pp.1057-1067
DOI: 10.3233/JAD-142820
PMID: 26401783
PMCID: PMC6287610
NLM abbreviation: J Alzheimers Dis
ISSN: 1387-2877
eISSN: 1875-8908
Language: English
Date published: 2015
Academic Unit: Radiology; Pharmaceutical Sciences and Experimental Therapeutics
Record Identifier: 9984051798802771

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