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Classification of Mild Cognitive Impairment With Multimodal Data Using Both Labeled and Unlabeled Samples
- Source :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18:2281-2290
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Mild Cognitive Impairment (MCI) is a preclinical stage of Alzheimer's Disease (AD) and is clinical heterogeneity. The classification of MCI is crucial for the early diagnosis and treatment of AD. In this study, we investigated the potential of using both labeled and unlabeled samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to classify MCI through the multimodal co-training method. We utilized both structural magnetic resonance imaging (sMRI) data and genotype data of 364 MCI samples including 228 labeled and 136 unlabeled MCI samples from the ADNI-1 cohort. First, the selected quantitative trait (QT) features from sMRI data and SNP features from genotype data were used to build two initial classifiers on 228 labeled MCI samples. Then, the co-training method was implemented to obtain new labeled samples from 136 unlabeled MCI samples. Finally, the random forest algorithm was used to obtain a combined classifier to classify MCI patients in the independent ADNI-2 dataset. The experimental results showed that our proposed framework obtains an accuracy of 85.50 percent and an AUC of 0.825 for MCI classification, respectively, which showed that the combined utilization of sMRI and SNP data through the co-training method could significantly improve the performances of MCI classification.
- Subjects :
- Male
Databases, Factual
Multimodal data
0206 medical engineering
02 engineering and technology
Polymorphism, Single Nucleotide
behavioral disciplines and activities
Neuroimaging
Alzheimer Disease
mental disorders
Clinical heterogeneity
Genetics
Humans
Medicine
Cognitive Dysfunction
Diagnosis, Computer-Assisted
Cognitive impairment
Aged
Aged, 80 and over
Snp data
Co-training
business.industry
Applied Mathematics
Pattern recognition
Magnetic Resonance Imaging
Random forest
Female
Artificial intelligence
Preclinical stage
business
human activities
Algorithms
020602 bioinformatics
Biotechnology
Subjects
Details
- ISSN :
- 23740043 and 15455963
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Accession number :
- edsair.doi.dedup.....b45ecf23b7f40cff1728d9b1eef05116