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Updated: Nov 20, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Classification of Mild Cognitive Impairment With Multimodal Data Using Both Labeled and Unlabeled Samples
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.
Insights
Classifying Mild Cognitive Impairment (MCI) is key for early Alzheimer's Disease (AD) detection. Combining MRI and genetic data with co-training significantly improved MCI classification accuracy to 85.50%.
Area of Science:
- Neuroscience
- Medical Imaging
- Genetics
Background:
- Mild Cognitive Impairment (MCI) represents a preclinical stage of Alzheimer's Disease (AD).
- Accurate MCI classification is vital for timely AD diagnosis and intervention.
- MCI exhibits significant clinical heterogeneity, complicating diagnosis.
Purpose of the Study:
- To investigate the efficacy of a multimodal co-training method for MCI classification.
- To leverage both labeled and unlabeled data for improved diagnostic performance.
- To integrate structural magnetic resonance imaging (sMRI) and genotype data for enhanced MCI detection.
Main Methods:
- Utilized 364 MCI samples (228 labeled, 136 unlabeled) from the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) cohort.
- Extracted quantitative trait (QT) features from sMRI and SNP features from genotype data.
- Implemented a multimodal co-training framework with random forest classification on an independent ADNI-2 dataset.
Main Results:
- Achieved an accuracy of 85.50% for MCI classification.
- Obtained an Area Under the Curve (AUC) of 0.825.
- Demonstrated that combined sMRI and SNP data significantly improve MCI classification performance.
Conclusions:
- The multimodal co-training approach effectively utilizes both labeled and unlabeled data.
- Integration of sMRI and genotype data offers a powerful strategy for MCI classification.
- This framework shows significant potential for early and accurate diagnosis of MCI.
