Depression Level Classification Using Compact Cross-Domain Feature Engineering on Sleep, Physical Activity, and Demographic Data

Depression is a common mental health disorder and a major public health concern, and early identification of depressive symptoms using population survey data can support exploratory risk analysis. However, many previous studies formulated depression prediction as a binary classification task. They used broad predictor sets, while multiclass depression-level classification with compact and interpretable cross-domain features remains less explored. This study developed a compact cross-domain feature engineering approach for classifying depression levels using sleep, physical activity, and demographic data from NHANES 2017–2018. A total of 5,068 respondents were included after preprocessing and PHQ-9 label construction. The target variable was divided into three classes: no-to-minimal depression, mild depression, and depression. Twenty raw predictors were transformed into 15 engineered features representing sleep patterns, sleep-related problems, physical activity, sedentary behavior, and interactions with age and income. Logistic Regression with class_weight = balanced was evaluated using stratified 5-fold cross-validation and compared with several baseline classifiers. The Final 15 FE Only scenario achieved an accuracy of 0.6215 ± 0.0091, macro F1-score of 0.4501 ± 0.0104, balanced accuracy of 0.5146 ± 0.0179, and depression-class recall of 0.6122 ± 0.0622. Compared with Raw Features, depression-class recall increased from 0.5360 ± 0.0541 to 0.6122 ± 0.0622, although the improvement was not statistically significant. These findings indicate that compact cross-domain features can improve sensitivity toward the depression class in an interpretable Logistic Regression setting, but overall predictive gains remain modest. The proposed model is more suitable for exploratory and population-level screening support rather than a stand-alone clinical diagnosis