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From
Using Statistical Machine Learning to Find Complex Interactions and Important CVD Risk Factors When Predicting General Health in Adults
Peter D. Hart
American Journal of Public Health Research
.
2025
, 13(3), 90-102 doi:10.12691/ajphr-13-3-1
Table 1. Sample characteristics by general health (GH) status, NHANES 2017-2018
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Table S1. (Supplement). Sample characteristics by general health (GH) status, NHANES 2015-2016
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Table 2. Cardiovascular disease (CVD) variables and covariates by general health (GH) status, NHANES 2017-2018
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Table 3. Correlations between general health (GH) and study variables, NHANES 2017-2018
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Table 4. Decision tree leaf paths predicting general health (GH) with cardiovascular disease (CVD) risk factor variables, NHANES 2017-2018
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Table 5. Decision tree leaf/node differences using linear regression with training and test datasets
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Table 6. Performance statistics for decision tree and random forest models predicting general health (GH)
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Table 7. Variable rankings for decision tree and random forest models predicting general health (GH), NHANES 2017-2018
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Table S2. (Supplement). Cardiovascular disease (CVD) variables and covariates by general health (GH) status, NHANES 2015-2016
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Table S3. (Supplement). Correlations between general health (GH) and study variables, NHANES 2015-2016
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