A Study of Dementia Prediction Models Based on Machine Learning with Survey Data of Community-Dwelling Elderly People in China

Abstract

Background: For community-dwelling elderly individuals without enough clinical data, it is important to develop a method to predict their dementia risk and identify risk factors for the formulation of reasonable public health policies to prevent dementia. Objective: A community elderly survey data was used to establish machine learning prediction models for dementia and analyze the risk factors. Methods: In a cluster-sample community survey of 9,387 elderly people in 5 subdistricts of Wuxi City, data on sociodemographics and neuropsychological self-rating scales for depression, anxiety, and cognition evaluation were collected. Machine learning models were developed to predict their dementia risk and identify risk factors. Results: The random forest model (AUC = 0.686) had slightly better dementia prediction performance than logistic regression model (AUC = 0.677) and neural network model (AUC = 0.664). The sociodemographic data and psychological evaluation revealed that depression (OR = 3.933, 95% CI = 2.995–5.166); anxiety (OR = 2.352, 95% CI = 1.577–3.509); multiple physical diseases (OR = 2.486, 95% CI = 1.882–3.284 for three or above); “disability, poverty or no family member” (OR = 1.859, 95% CI = 1.337–2.585) and “empty nester” (OR = 1.339, 95% CI = 1.125–1.595) in special family status; “no spouse now” (OR = 1.567, 95% CI = 1.118–2.197); age older than 80 years (OR = 1.645, 95% CI = 1.335–2.026); and female (OR = 1.214, 95% CI = 1.048–1.405) were risk factors for suspected dementia, while a higher education level (OR = 0.365, 95% CI = 0.245–0.546 for college or above) was a protective factor. Conclusion: The machine learning models using sociodemographic and psychological evaluation data from community surveys can be used as references for the prevention and control of dementia in large-scale community populations and the formulation of public health policies.