Extracting Behavioral Rules from Health Survey Data with Interpretable Models

This study investigates the use of interpretable machine learning techniques to identify behavioral and demographic patterns associated with diabetes, based on structured population survey data from the Canadian Community Health Survey (CCHS). A decision tree classifier was applied to a dataset comprising 16,824 respondents and 38 preprocessed features covering lifestyle, well-being, and sociodemographic factors. The model was optimized through grid search with five-fold stratified cross-validation, achieving a test accuracy of 61.3% (mean 62.6% ±0.6% across a 10×5 repeated stratified cross-validation). Feature importance analysis revealed that age, alcohol consumption patterns, daily energy expenditure, and physical activity were the most influential factors associated with diabetes status, with the top three features exhibiting stable importance across all cross-validation folds. The model produced a set of 32 human-readable decision rules; a sensitivity analysis confirmed that these rules are stable across encoding choices and cross-validation folds. Several model variants were evaluated: a class-weighted decision tree, a logistic regression baseline, an age-only decision tree, and an age and sex logistic regression. The class-weighted model improved minority-class recall (from 0.25 to 0.53) at the cost of overall accuracy. A one-hot encoding sensitivity analysis showed that replacing ordinal label encoding of nominal variables with one-hot encoding produces virtually identical results (accuracy: 61.4% vs. 61.3%), confirming that the main rules are not artifacts of the encoding choice. Although the classification accuracy is moderate and not significantly better than a majority-class baseline (McNemar’s test, p=0.455), the extracted rules confirmed several known associations and revealed interactions between social and lifestyle variables. These rules are intended as hypothesis-generating population-level descriptors rather than validated clinical decision tools, and no causal inference is claimed. This approach demonstrates the value of rule-based models for exploratory public health research.