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CMC: A consensus multi-view clustering model for predicting Alzheimer's disease progression.

Xiaobo Zhang1, Yan Yang1, Tianrui Li1

  • 1School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China; Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China.

Computer Methods and Programs in Biomedicine
|December 20, 2020
PubMed
Summary

This study introduces Consensus Multi-view Clustering (CMC) for Alzheimer's Disease (AD) staging using multi-view data. The model effectively predicts AD progression stages by integrating diverse data features, improving diagnostic accuracy.

Keywords:
Alzheimer’s disease (AD) progressionConsensus representationMagnetic resonance imaging (MRI)Multi-view clusteringNonnegative matrix factorization (NMF)

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Area of Science:

  • Artificial Intelligence
  • Neuroscience
  • Medical Imaging

Background:

  • Machine learning aids Alzheimer's Disease (AD) diagnosis but often uses single data types and binary classification.
  • Existing methods struggle with multi-view data integration and manual parameter tuning for complex AD progression.
  • Multi-view learning offers enhanced feature representation for more robust diagnostic models.

Purpose of the Study:

  • To develop a novel multi-view clustering model, Consensus Multi-view Clustering (CMC), for predicting multiple stages of Alzheimer's Disease (AD) progression.
  • To leverage multi-view learning to fully capture features from limited medical imaging data.
  • To improve AD prediction accuracy and enable classification of different AD phases.

Main Methods:

  • Proposed a Consensus Multi-view Clustering (CMC) model utilizing nonnegative matrix factorization.
  • Employed multi-view learning to capture comprehensive data features and similarity relations.
  • Developed a consensus representation integrating shared and complementary knowledge from multiple data views without manual parameter setting.

Main Results:

  • The CMC model effectively integrates multi-view data, overcoming limitations of single-view approaches.
  • Experimental results demonstrated improved prediction performance for Alzheimer's Disease (AD) progression.
  • The model successfully screened and classified symptoms across different AD phases using brain MRI data.

Conclusions:

  • Consensus Multi-view Clustering (CMC) offers a powerful approach for multi-stage Alzheimer's Disease (AD) prediction.
  • The model's ability to fuse multi-view data enhances feature representation and diagnostic capabilities.
  • This method shows significant potential for advancing Alzheimer's Disease research and clinical application.