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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Multiclass classification of Alzheimer's disease prodromal stages using sequential feature embeddings and regularized

Oyekanmi O Olatunde1, Kehinde S Oyetunde2, Jihun Han3

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Accurately classifying neurodegeneration stages (cognitive normal, mild cognitive impairment, Alzheimer's disease) is vital. A new framework combining imaging and clinical data achieved state-of-the-art multiclass classification accuracy, outperforming previous methods.

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Area of Science:

  • Neuroscience
  • Medical Imaging Analysis
  • Machine Learning for Healthcare

Background:

  • Early detection of neurodegenerative diseases like Alzheimer's disease (AD) is critical for timely intervention.
  • Mild cognitive impairment (MCI) presents heterogeneous features, challenging accurate classification across cognitive normal (CN), MCI, and AD stages.
  • Existing classification methods often rely on pairwise binary comparisons, complicating direct multiclass evaluation and interpretation.

Purpose of the Study:

  • To develop a robust framework for direct multiclass classification of neurodegenerative disease stages (CN vs. MCI vs. AD).
  • To overcome the limitations of binary classification approaches in handling the heterogeneity of MCI data.
  • To establish a new state-of-the-art (SOTA) performance benchmark for multiclass classification in neurodegeneration detection.

Main Methods:

  • A novel framework integrating unsupervised ensemble manifold regularized sparse low-rank approximation with a regularized multikernel support vector machine (SVM).
  • Extraction of joint feature embeddings from MRI and PET neuroimaging data.
  • Combination of imaging features with clinical data (Apoe4, Adas11, MPACC digits, Intracranial volume) for classification using a regularized multikernel SVM.

Main Results:

  • Achieved SOTA performance in CN vs. MCI vs. AD multiclass classification with a mean accuracy of 84.87±6.09 and an F1 score of 84.83±6.12.
  • Demonstrated strong generalization to binary classification tasks, achieving SOTA results across most categories.
  • The CN vs. MCI binary classification showed a minimal performance decrease of 0.2% compared to existing best scores.

Conclusions:

  • The proposed framework effectively addresses the challenge of classifying heterogeneous neurodegenerative disease stages.
  • This integrated approach offers a more accurate and interpretable method for multiclass classification compared to sequential binary tasks.
  • The findings pave the way for improved diagnostic tools in early neurodegeneration detection and intervention.