Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction

Topic: Risk, prevention and care in population studies

The Level 3 model achieved high classification performance (AUC: 93.98%), and the Level 1 model achieved comparable performance (AUC: 91.53%). For MCI-to-dementia progression, the framework showed strong prognostic performance. The Level 3 and Level 1 models achieved AUCs of 85.90% and 81.90% for predicting progression over a 2-year window, respectively. Key predictors remained consistent across all resource levels, such as difficulty managing finances and self-reported cognitive decline, and sociodemographic factors, including education level and Black race. The tiered system offers a scalable and accessible approach for dementia detection and prognostic stratification. It helps non-specialists conduct initial evaluations and identify high-risk individuals with minimal data. High-risk patients may then be triaged for specialized assessment. This resource-stratified framework offers a strategy to expand diagnostic capacity and reduce care inequities, especially in low-resource settings.

Key findings

• The Level 3 model achieved high classification performance (AUC: 93.98%), and the Level 1 model achieved comparable performance (AUC: 91.53%).
• For MCI-to-dementia progression, the framework showed strong prognostic performance.
• The Level 3 and Level 1 models achieved AUCs of 85.90% and 81.90% for predicting progression over a 2-year window, respectively.
• Key predictors remained consistent across all resource levels, such as difficulty managing finances and self-reported cognitive decline, and sociodemographic factors, including education level and Black race.
• The tiered system offers a scalable and accessible approach for dementia detection and prognostic stratification.

Why it matters

A tiered machine learning framework classified cognitive status and predicted progression from mild cognitive impairment to dementia with strong performance, using predictors like difficulty managing finances and self-reported cognitive decline. It is designed to help non-specialists conduct initial evaluations and triage high-risk patients for specialized assessment, especially in low-resource settings.

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Study details

• Participants31,081
• Effect sizeAUC: 93.98; AUC: 91.53; 85.90%
• Evidence strengthLow

Read the original

• DOI: 10.1186/s13195-026-02006-7
• PubMed: 41794745
• Original source →
• Free full text: Alzheimer s Research & Therapy →

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

@article{yang2026-41794745,
  author  = {Yang, Jingmei and Ding, Huitong and Amini, Samad and Hao, Boran and Karjadi, Cody and Au, Rhoda and Paschalidis, Ioannis Ch.},
  title   = {{Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction}},
  journal = {Alzheimer s Research \& Therapy},
  year    = {2026},
  doi     = {10.1186/s13195-026-02006-7},
  pmid    = {41794745},
  url     = {https://doi.org/10.1186/s13195-026-02006-7}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 20.

• Multidomain lifestyle intervention benefits a large elderly population at risk for cognitive decline and dementia regardless of baseline characteristics: The FINGER trial Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017-10-19 · usefulness score 77/100 · Prevention and risk
• The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER): study design and progress Alzheimer's & dementia : the journal of the Alzheimer's Association · 2013-01-17 · usefulness score 76/100 · Prevention and risk
• A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial The Lancet · 2015-03-12 · usefulness score 74/100 · Prevention and risk
• Effects of intensive lifestyle changes on the progression of mild cognitive impairment or early dementia due to Alzheimer's disease: a randomized, controlled clinical trial Alzheimer's research & therapy · 2024-06-07 · usefulness score 74/100 · Mild cognitive impairment
• Version 3 of the National Alzheimer's Coordinating Center's Uniform Data Set Alzheimer disease and associated disorders · 2018-10-01 · usefulness score 73/100 · Getting a diagnosis
• Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023-08-10 · usefulness score 71/100 · Digital health tools
• AI-based differential diagnosis of dementia etiologies on multimodal data Nature medicine · 2024-07-04 · usefulness score 71/100 · Digital health tools
• Alzheimer's Association clinical practice guideline for the Diagnostic Evaluation, Testing, Counseling, and Disclosure of Suspected Alzheimer's Disease and Related Disorders (DETeCD-ADRD): Executive summary of recommendations for primary care Alzheimer's & Dementia · 2024-12-23 · usefulness score 70/100 · Getting a diagnosis
• Missed and delayed diagnosis of dementia in primary care: prevalence and contributing factors Alzheimer disease and associated disorders · 2009-10-01 · usefulness score 67/100 · Getting a diagnosis
• Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions Journal of medical systems · 2023-02-01 · usefulness score 62/100 · Digital health tools
• 2024 Alzheimer's disease facts and figures Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024-04-30 · usefulness score 62/100 · Caregiving
• Dementia Prevention and Treatment: A Narrative Review JAMA internal medicine · 2024-05-01 · usefulness score 62/100 · Treatments

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• Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review Medical image analysis · 2020-10-06 · usefulness score 70/100
• Integrative and interpretable framework to unveil the neurophysiological fingerprint of Alzheimer’s disease and mild cognitive impairment: A machine learning-SHAP approach Journal of Applied Biomedicine · 2025-06-13 · usefulness score 38/100
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• AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease Journal of biomedical informatics · 2023-07-08 · usefulness score 50/100

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• The Montreal Cognitive Assessment (MoCA) - a sensitive screening instrument for detecting cognitive impairment in chronic hemodialysis patients PloS one · 2014-10-27 · usefulness score 49/100 · Cognitive tests and assessment
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• A review that covers this ground Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease Alzheimer's research & therapy · 2023-10-14 · usefulness score 63/100
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