Development and Validation of a Machine Learning-Based Dementia Screening Tool: The Six-Question Dementia Screening Test

Abstract

Timely detection of dementia is crucial for reducing its health and societal burden. Standard tools such as the Mini-Mental State Examination (MMSE) and Cognitive Abilities Screening Instrument (CASI), although widely used, are limited by time and resource demands. This study developed and validated a machine learning–based screening tool using the Six-Question Dementia Screening Test (6Q-DS), a brief interview of six items. Data from 533 older adults at a neurology clinic in Taiwan (331 with dementia, 202 without) were analyzed with eXtreme Gradient Boosting. The 6Q-DS achieved an AUC of 0.936, sensitivity 0.879, specificity 0.951, and accuracy 0.907 for dementia vs non-dementia. For identifying very mild dementia vs non-dementia, the AUC was 0.874, with a sensitivity of 0.818, specificity of 0.805, and accuracy of 0.810. Comparable to MMSE and CASI, the 6Q-DS provides a practical, rapid, and user-friendly tool for dementia screening.