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Updated: Jun 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development and validation of a dementia risk prediction model for low- and middle-income countries: the 10/66 study
Eduwin Pakpahan1, Zhongyang Guan2,3, Mario Siervo2,3
1Applied Statistics & Data Science Research Group, School of Engineering, Physics, and Mathematics, Northumbria University, College Street, Newcastle upon Tyne, NE1 8ST, United Kingdom.
Background:
Most people with dementia live in LMICs, underscoring the need for LMIC-specific identification of high-risk individuals. This study aimed to develop and validate a simple dementia risk prediction model for these settings.
Methods:
Data from seven 10/66 Study sites were analyzed. Over 100 candidate predictors were screened based on existing models and the 2024 Lancet Commission, including LMIC-specific variables (eg, food insecurity and household assets). Predictors were selected using LASSO and modelled with the Fine-Gray method to generate a risk score. Predictive accuracy was pooled via meta-analysis.
Results:
11143 participants were included, among whom 1069 (9.6%) developed dementia during follow-up. A five-factor risk score comprising age, social engagement, physical activity, hypertension, and difficulty in handling money was developed. The pooled c-statistic was 0.75 (95% CI: 0.72-0.78), with good calibration across sites. Decision curve analysis showed a modest net benefit, with variation across countries.
Conclusion:
It is possible to predict incident dementia with reasonable accuracy using a simple model across different LMICs. Our findings support the use of context-specific risk assessment tools to identify individuals at elevated dementia risk in LMIC settings, which may inform resource allocation for dementia care services and public health planning.
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