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Journal of Economic Theory and Econometrics
Journal of the Korean Econometric Society
Long-term Predictors of Cardiovascular Disease: A Machine Learning Approach
Vol.34, No.4, December 2023, 86–114
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Young-Joo Kim
(Hongik University)
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Abstract
This study investigates long-term cardiovascular disease (CVD) risk
predictors for middle-aged and older adults in Korea. Using the Least Absolute
Shrinkage and Selection Operator (Lasso) and the double-selection Lasso, this
study provides novel evidence that Body Mass Index (BMI) is a single risk factor
with long-term predictability for CVD odds ratio, selected apart from age, which
is non-modifiable. The lasting effect of BMI on CVD risk remains robust and
consistent across different methods and specifications that account for variable
selection errors in high-dimensional logit regression and BMI’s time trends. In
addition to the long-term predictive role of BMI in CVD risk, the disease burden
associated with increased BMI is quantified by comparing the marginal effects of
BMI to those of age across various groups. The marginal effect of elevated BMI
is more pronounced in men than women and among the employed compared
to the non-employed. Leading a healthy lifestyle through the control of BMI
is a critical element for preventing CVD based on the empirical findings of the
current study.
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Keywords
BMI, CVD, Lasso, odds ratio, marginal effect |
JEL classification codes
I12, C55 |
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