Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study

Topic: Risk, prevention and care in population studies

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.

Key findings

• 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).

Why it matters

Unsupervised machine learning identified a group in population surveys with a high likelihood of dementia without needing clinical diagnosis in a subsample. It is a detection method and reports no treatment, cure, or prevention.

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Study details

• Participants18,165
• Effect sizeAUC=0.91; AUC=0.90; 39.2%
• Evidence strengthLow

Read the original

• DOI: 10.2196/10493
• PubMed: 29986849
• Original source →
• Free full text: Journal of Medical Internet Research →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{cleretdelangavant2018-29986849,
  author  = {Cleret de Langavant, L. and Bayen, E. and Yaffe, K.},
  title   = {{Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study}},
  journal = {Journal of medical Internet research},
  year    = {2018},
  doi     = {10.2196/10493},
  pmid    = {29986849},
  url     = {https://doi.org/10.2196/10493}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Monetary Costs of Dementia in the United States New England Journal of Medicine · 2013-04-04 · usefulness score 66/100 · Costs and economics
• Physical activity, cognitive decline, and risk of dementia: 28 year follow-up of Whitehall II cohort study BMJ · 2017-06-22 · usefulness score 66/100 · Prevention and risk
• The Aging, Demographics, and Memory Study: study design and methods Neuroepidemiology · 2005-08-15 · usefulness score 60/100 · Getting a diagnosis
• Hearing Loss and Cognitive Decline in Older Adults JAMA Internal Medicine · 2013-01-21 · usefulness score 57/100 · Risk factors
• Modifiable Risk Factors for Prevention of Dementia in Midlife, Late Life and the Oldest-Old: Validation of the LIBRA Index Journal of Alzheimer's disease : JAD · 2017-01-01 · usefulness score 56/100 · Prevention and risk
• A short form of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): development and cross-validation Psychological medicine · 1994-02-01 · usefulness score 49/100 · Getting a diagnosis

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