Predicting Progression to Dementia Using Auditory Verbal Learning Test in Community-Dwelling Older Adults Based On Machine Learning

Background: Primary healthcare institutions find identifying individuals with dementia particularly challenging. This study aimed to develop machine learning models for identifying predictive features of older adults with normal cognition to develop dementia. Methods: We developed four machine learning models:logistic regression, decision tree, random forest, and gradient-boosted trees, predicting dementia of 1,162 older adults with normal cognition at baseline from the Hubei Memory and Aging Cohort Study. All relevant variables collected were included in the models. The Shanghai Aging Study was selected as a replication cohort (n = 1,370) to validate the performance of models including the key features after a wrapper feature selection technique. Both cohorts adopted comparable diagnostic criteria for dementia to most previous cohort studies. Results: The random forest model exhibited slightly better predictive power using a series of auditory verbal learning test, education, and follow-up time, as measured by overall accuracy (93%) and an area under the curve (AUC) (mean [standard error]: 088 [0.07]). When assessed in the external validation cohort, its performance was deemed acceptable with an AUC of 0.81 (0.15). Conversely, the logistic regression model showed better results in the external validation set, attaining an AUC of 0.88 (0.20). Conclusion: Our machine learning framework offers a viable strategy for predicting dementia using only memory tests in primary healthcare settings. This model can track cognitive changes and provide valuable insights for early intervention.