Identifying Risk Factors for Cognitive Decline Using Statistical Learning Techniques and Functional Data Analysis
Date
2022-09-26
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Degree Level
Masters
Abstract
Background: Numerous studies have shown that older adults’ cognitive abilities are age-related and likely to decline at a certain age. Based on these indications, this work uses functional principal component analysis (FPCA) to explore changes in cognitive function with age. This study aims to describe the longitu- dinal cognitive function of elderly trajectory patterns between 65 and 80 years of baseline age using FPCA and identify risk factors for cognitive decline using machine learning algorithms.
Methods: We used FPCA to extract the overall pattern change and use elastic-net, decision tree, and random forest models to find risk factors. In a sample of elderly (n = 944) with 6608 measurements (7 waves) from the Survey of Health, Aging, and Retirement in Europe (SHARE), by using age at interview as time, longitudinal cognitive function trajectory patterns for the elderly were extracted using FPCA. Zou and Hastie (2005) proposed the elastic net regression method, which effectively implements feature selection by setting coefficients of non-significant variables to zero [56]. Random forest is a tree-based machine learning algorithm that harnesses the power of multiple decision trees to make decisions. Random forests combine the outputs of individual decision trees to generate the final result. [15]. We modelled the first two functional principal components (FPCs) with these machine learning algorithms and used the selected covariates in the baseline wave to identify risk factors.
Results and Conclusions: We have obtained four FPCs explained by 78.0, 14.2, 6.7 and 1.1 % of the variation respectively for the cognitive function. The mean function of FPCA shows that cognitive decline is generally divided into two stages (early decline and late decline). By analyzing and comparing a set of models at the national level, the cognitive function of each country is slightly different. Older people in Italy and Spain have significantly lower cognitive abilities. The Predictive R2 of FPC1 and FPC2 is around 0.5 and 0.2 with covariate delayed recall score (DRS) and reduced to 0.4 and 0.1 without covariate DRS. From the individual point of view, many risk factors are modifiable and can be prevented in advance. Our results show that immediate recall score, education level, country, numeracy score, reading score, and household income are associated with cognitive patterns in the elderly.
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Keywords
FPCA, cognitive decline
Citation
Degree
Master of Science (M.Sc.)
Department
Mathematics and Statistics
Program
Mathematics