{#sec:candid}
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Because some of the content of the NHIS questionnaire changed over the years depending on the data needs for current health topics, we decided to include questions that were consistent across all 10 years (1,022 variables out of 2,360 total). We then excluded those variables that were only asked to the participant conditional on a prior answer to another question (811 variables, labeled as “daughter variables”), those that had $>80$% missingness (8 variables), and those that contained redundant information with other variables (for example, identifiers, repeated information; 45 variables). The final dataset had 211 variables, of which 188 were interview variables (candidate variables for the model) and 23 were identifiers and outcome variables. For the 10 continuous variables with missing values (ranging from 1%–27% missing rates), we used single-value imputation with median. For 11 categorical variables with missing values (ranging from 3%–78% missing rates) we created “missing” as a separate category. Finally, we added the daughter questions (85 variables) for the top 25 variables most correlated with outcome and for the top 25 variables identified using an XGBoost model, increasing the total number of candidate variables to 273 variables. Categorical variables were one-hot encoded.

Adaptive Feature Selection using 