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. 2020;75(3):717-728.
doi: 10.3233/JAD-190967.

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach

Damaris Aschwanden  1 , Stephen Aichele  2   3 , Paolo Ghisletta  2   4   5 , Antonio Terracciano  1 , Matthias Kliegel  2   5 , Angelina R Sutin  1 , Justin Brown  1 , Mathias Allemand  6   7
Affiliations

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach

Damaris Aschwanden et al. J Alzheimers Dis. 2020.

Abstract

Background: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking.

Objective: We used a combined methodology of machine learning and semi-parametric survival analysis to estimate the relative importance of 52 predictors in forecasting cognitive impairment and dementia in a large, population-representative sample of older adults.

Methods: Participants from the Health and Retirement Study (N = 9,979; aged 50-98 years) were followed for up to 10 years (M = 6.85 for cognitive impairment; M = 7.67 for dementia). Using a split-sample methodology, we first estimated the relative importance of predictors using machine learning (random forest survival analysis), and we then used semi-parametric survival analysis (Cox proportional hazards) to estimate effect sizes for the most important variables.

Results: African Americans and individuals who scored high on emotional distress were at relatively highest risk for developing cognitive impairment and dementia. Sociodemographic (lower education, Hispanic ethnicity) and health variables (worse subjective health, increasing BMI) were comparatively strong predictors for cognitive impairment. Cardiovascular factors (e.g., smoking, physical inactivity) and polygenic scores (with and without APOEɛ4) appeared less important than expected. Post-hoc sensitivity analyses underscored the robustness of these results.

Conclusions: Higher-order factors (e.g., emotional distress, subjective health), which reflect complex interactions between various aspects of an individual, were more important than narrowly defined factors (e.g., clinical and behavioral indicators) when evaluated concurrently to predict cognitive impairment and dementia.

Keywords: Aging; Cox proportional hazard survival analysis; cognitive impairment; dementia; machine learning; protective factors; random forest survival analysis; risk factors.

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Conflict of interest statement

DECLARATION OF CONFLICTING INTERESTS

The authors declare no potential conflicts of interest concerning the research, the authorship, and publication of this article.

Figures

Figure 1.
Figure 1.
The most important predictors for cognitive impairment (Part A) and dementia (Part B). The relative variable importance (as determined by random forest survival analysis) is graphed on the y-axis, ranging from 0 (lowest importance) to 1 (highest importance). The hazard ratios (as determined by the Cox PH survival analysis) are shown on the x-axis, ranging from 0 to 3. The colors of the dots indicate the factors that were significantly related to an increased (red) or decreased (green) risk.
Figure 1.
Figure 1.
The most important predictors for cognitive impairment (Part A) and dementia (Part B). The relative variable importance (as determined by random forest survival analysis) is graphed on the y-axis, ranging from 0 (lowest importance) to 1 (highest importance). The hazard ratios (as determined by the Cox PH survival analysis) are shown on the x-axis, ranging from 0 to 3. The colors of the dots indicate the factors that were significantly related to an increased (red) or decreased (green) risk.
See this image and copyright information in PMC

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