TY  - JOUR
AU  - Cleret de Langavant, Laurent
AU  - Bayen, Eleonore
AU  - Yaffe, Kristine
PY  - 2018
DA  - 2018/07/09
TI  - Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study
JO  - J Med Internet Res
SP  - e10493
VL  - 20
IS  - 7
KW  - dementia
KW  - cognition disorders
KW  - health surveys
KW  - electronic health records
KW  - diagnosis
KW  - unsupervised machine learning
KW  - cluster analysis
KW  - data mining
AB  - Background: Dementia is increasing in prevalence worldwide, yet frequently remains undiagnosed, especially in low- and middle-income countries. Population-based surveys represent an underinvestigated source to identify individuals at risk of dementia. Objective: The aim is to identify participants with high likelihood of dementia in population-based surveys without the need of the clinical diagnosis of dementia in a subsample. Methods: Unsupervised machine learning classification (hierarchical clustering on principal components) was developed in the Health and Retirement Study (HRS; 2002-2003, N=18,165 individuals) and validated in the Survey of Health, Ageing and Retirement in Europe (SHARE; 2010-2012, N=58,202 individuals). Results: Unsupervised machine learning classification identified three clusters in HRS: cluster 1 (n=12,231) without any functional or motor limitations, cluster 2 (N=4841) with walking/climbing limitations, and cluster 3 (N=1093) with both functional and walking/climbing limitations. Comparison of cluster 3 with previously published predicted probabilities of dementia in HRS showed that it identified high likelihood of dementia (probability of dementia >0.95; area under the curve [AUC]=0.91). Removing either cognitive or both cognitive and behavioral measures did not impede accurate classification (AUC=0.91 and AUC=0.90, respectively). Three clusters with similar profiles were identified in SHARE (cluster 1: n=40,223; cluster 2: n=15,644; cluster 3: n=2335). Survival rate of participants from cluster 3 reached 39.2% (n=665 deceased) in HRS and 62.2% (n=811 deceased) in SHARE after a 3.9-year follow-up. Surviving participants from cluster 3 in both cohorts worsened their functional and mobility performance over the same period. Conclusions: Unsupervised machine learning identifies high likelihood of dementia in population-based surveys, even without cognitive and behavioral measures and without the need of clinical diagnosis of dementia in a subsample of the population. This method could be used to tackle the global challenge of dementia. 
SN  - 1438-8871
UR  - http://www.jmir.org/2018/7/e10493/
UR  - https://doi.org/10.2196/10493
UR  - http://www.ncbi.nlm.nih.gov/pubmed/29986849
DO  - 10.2196/10493
ID  - info:doi/10.2196/10493
ER  -