Journal Article

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

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

2026

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Aged / Aged, 80 and over / Alzheimer's disease / Analysis / Batteries / Biomedical and Life Sciences / Biomedicine / Boosting Machine Learning Algorithms / Cerebrospinal fluid / Classification / Classification Algorithms / Cognitive ability / Cognitive Assessment / Cognitive Dysfunction - classification / Cognitive Dysfunction - diagnosis / Dementia / Dementia - classification / Dementia - diagnosis / Developing countries / Development and progression / Discrimination in medical care / Disease Progression / Equipment and supplies / Feature selection / Female / Geriatric Psychiatry / Geriatrics/Gerontology / Health care disparities / Healthcare Disparities / Humans / LDCs / Machine Learning / Male / MCI-to-Dementia Progression / Medical imaging / Memory / Mild cognitive impairment / National Alzheimer’s Coordinating Center Data / Neuroimaging / Neurology / Neuropsychological Tests / Neuropsychology / Neurosciences / Outpatient care facilities / Prediction Algorithms / Predictive Learning Models / Primary care / Prognosis / Questionnaires / Research centers / Resource-Stratified Screening / Teaching / Variance analysis