An explainable machine learning approach for Alzheimer's disease classification

Topic: Amyloid, tau and blood-test diagnosis

The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for multiclass task. To enhance model explainability, we employed two rule-extraction approaches: class rule mining and stable and interpretable rule set for classification model. These approaches generated human-understandable rules to assist domain experts in comprehending the key factors involved in AD development. We further validated these rules using SHAP and LIME models, underscoring the significance of factors such as MEMORY, JUDGMENT, COMMUN, and ORIENT in determining AD risk. Our experimental outcomes also shed light on the crucial role of the Clinical Dementia Rating tool in predicting AD.

Key findings

• The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked.

Why it matters

This study used machine learning on a large dataset to classify Alzheimer's disease and predict its progression over 4 years, with support vector machine models showing high performance. The authors also generated human-understandable rules to help experts understand the key factors involved in classification.

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Study details

• Effect size98.9%; 90.7%; 72.8%
• Evidence strengthLow

Read the original

• DOI: 10.1038/s41598-024-51985-w
• PubMed: 38302557
• Original source →
• Free full text: Scientific Reports →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{alatrany2024-38302557,
  author  = {Alatrany, A. S. and Khan, W. and Hussain, A. and Kolivand, H. and Al-Jumeily, D.},
  title   = {{An explainable machine learning approach for Alzheimer's disease classification}},
  journal = {Scientific reports},
  year    = {2024},
  doi     = {10.1038/s41598-024-51985-w},
  pmid    = {38302557},
  url     = {https://doi.org/10.1038/s41598-024-51985-w}
}

Studies this one cites

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• A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease Scientific reports · 2021-01-29 · usefulness score 76/100 · Digital health tools
• Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review Frontiers in aging neuroscience · 2017-10-06 · usefulness score 68/100 · Brain scans and imaging
• The Alzheimer's Disease Centers' Uniform Data Set (UDS): the neuropsychologic test battery Alzheimer disease and associated disorders · 2009-04-01 · usefulness score 62/100 · Disparities
• The Neuropsychiatric Inventory: assessing psychopathology in dementia patients Neurology · 1997-05-01 · usefulness score 57/100 · Caregiving
• Epidemiology of Alzheimer disease Nature reviews. Neurology · 2011-02-08 · usefulness score 54/100 · Getting a diagnosis
• Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs Scientific reports · 2022-10-17 · usefulness score 54/100 · Long-term cohort studies

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