Artificial Intelligence Approaches to Social Determinants of Cognitive Impairment and Its Associated Conditions

BACKGROUND AND PURPOSE: This study uses an artificial-intelligence model (recurrent neural network) for evaluating the following hypothesis: social determinants of disease association in a middle-aged or old population are different across gender and age groups. Here, the disease association indicates an association among cerebrovascular disease, hearing loss and cognitive impairment. METHODS: Data came from the Korean Longitudinal Study of Ageing (2014–2016), with 6,060 participants aged 53 years or more, that is, 2,556 men, 3,504 women, 3,640 aged 70 years or less (70−), 2,420 aged 71 years or more (71+). The disease association was divided into 8 categories: 1 category for having no disease, 3 categories for having 1, 3 categories for having 2, and 1 category for having 3. Variable importance, the effect of a variable on model performance, was used for finding important social determinants of the disease association in a particular gender/age group, and evaluating the hypothesis above. RESULTS: Based on variable importance from the recurrent neural network, important social determinants of the disease association were different across gender and age groups: 1) leisure activity for men; 2) parents alive, income and economic activity for women; 3) children alive, education and family activity for 70−; and 4) brothers/sisters cohabiting, religious activity and leisure activity for 70+. CONCLUSIONS: The findings of this study support the hypothesis, suggesting the development of new guidelines reflecting different social determinants of the disease association across gender and age groups.