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Background <p>Widespread access to diagnosis of cognitive decline remains inadequate. Assessment tools rely on neuroimaging, biofluid markers, or lengthy neuropsychological batteries. This reliance limits their use in primary care and low-resource settings and contributes to healthcare disparities. We developed and validated a three-level, resource-stratified machine learning framework to provide scalable dementia screening and prognostic risk stratification across diverse healthcare settings.</p> Methods <p>We used data from 31,081 participants in the National Alzheimer’s Coordinating Center. We trained Gradient Boosted Tree models for multi-class detection (Cognitively Intact/Mild Cognitive Impairment [MCI]/Dementia) and 10-year risk stratification (MCI-to-dementia progression). The framework tiered inputs by resource intensity. The minimal-resource Level 1 included demographic and basic functional data. The moderate-resource Level 2 added standard cognitive tests. The high-resource Level 3 added comprehensive neuropsychological batteries.</p> Results <p>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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Jingmei Yang,
  • Huitong Ding,
  • Samad Amini,
  • Boran Hao,
  • Cody Karjadi,
  • Rhoda Au,
  • Ioannis Ch. Paschalidis

摘要

Background

Widespread access to diagnosis of cognitive decline remains inadequate. Assessment tools rely on neuroimaging, biofluid markers, or lengthy neuropsychological batteries. This reliance limits their use in primary care and low-resource settings and contributes to healthcare disparities. We developed and validated a three-level, resource-stratified machine learning framework to provide scalable dementia screening and prognostic risk stratification across diverse healthcare settings.

Methods

We used data from 31,081 participants in the National Alzheimer’s Coordinating Center. We trained Gradient Boosted Tree models for multi-class detection (Cognitively Intact/Mild Cognitive Impairment [MCI]/Dementia) and 10-year risk stratification (MCI-to-dementia progression). The framework tiered inputs by resource intensity. The minimal-resource Level 1 included demographic and basic functional data. The moderate-resource Level 2 added standard cognitive tests. The high-resource Level 3 added comprehensive neuropsychological batteries.

Results

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.

Conclusions

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.