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Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and
Laurent Cleret de Langavant1,2,3,4,5, Eleonore Bayen5,6,7,8, Kristine Yaffe5,9
1Service de Neurologie, Hôpital Henri Mondor, Assistance Publique Hôpitaux de Paris, Créteil, France.
Journal of Medical Internet Research
|July 11, 2018
Summary
Machine learning can identify individuals at high risk of dementia using population surveys. This approach aids early detection, even without clinical diagnosis, addressing the global dementia challenge.
Area of Science:
- Gerontology
- Epidemiology
- Machine Learning
Background:
- Dementia prevalence is rising globally, with frequent underdiagnosis, particularly in low- and middle-income countries.
- Population-based surveys are an underutilized resource for identifying individuals at risk of dementia.
Purpose of the Study:
- To identify individuals with a high likelihood of dementia within population-based surveys.
- To achieve this without requiring a clinical diagnosis of dementia in a subsample.
Main Methods:
- Unsupervised machine learning (hierarchical clustering on principal components) was employed.
- The method was developed using the Health and Retirement Study (HRS) and validated in the Survey of Health, Ageing and Retirement in Europe (SHARE).
Main Results:
- Three distinct clusters were identified in both HRS and SHARE, with cluster 3 indicating a high likelihood of dementia (AUC=0.91 in HRS).
- Classification accuracy remained high even when cognitive or behavioral measures were excluded.
- Participants in cluster 3 exhibited higher mortality and worsening functional/mobility performance over follow-up.
Conclusions:
- Unsupervised machine learning effectively identifies a high likelihood of dementia in population-based surveys.
- This method is viable without cognitive/behavioral measures or clinical diagnosis in a subsample.
- The approach offers a potential solution to the global challenge of dementia.
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