Partitioned feature selection: locally uniform feature selection with structured feature grouping for explainable predictor identification

Introduction Feature selection helps identify important features in high-dimensional mental health data. However, conventional feature selection (CFS) methods commonly rank all features within a single feature space using an overall statistical or predictive criterion. This global evaluation method does not take into account the fact that symptoms of depression are interrelated and fall within various distinct clinical domains. To address this limitation, we propose partitioned feature selection (PFS), a framework that integrates domain-based feature grouping with group-wise feature selection. Methods PFS organizes depression symptoms into clinically relevant domains and applies four complementary feature selection methods to estimate individual feature importance within each domain. The importance scores are normalized and combined, then weighted according to each domain's association with global severity to calculate integrated group feature importance (IGFI). We evaluated PFS on two independent multiclass depression datasets using 16 classifiers. Validation-guided feature retention was used to select the final feature subsets. We also conducted a ten-trial random regrouping analysis, an exploratory Wilcoxon signed-rank comparison with CFS-RFE, and a comparison of IGFI rankings with SHAP explanations. Results PFS selected 21 features for Dataset 1 and 23 features for Dataset 2. Using the selected, untuned support vector classifier (SVC) configuration without synthetic minority over-sampling technique (SMOTE), PFS achieved test accuracies of 95.65% and 89.60% and Macro-F1 scores of 95.43% and 92.37%, respectively. The random regrouping analysis showed that the predictive effect of clinical partitioning varied between datasets. The exploratory Wilcoxon analysis also showed dataset-dependent statistical differences between PFS and CFS-RFE. Furthermore, IGFI and SHAP identified similar important features across clinical domains. Discussion PFS provides a structured approach to identifying important depression symptoms while preserving their clinical domain information. The findings indicate that domain-based grouping can support interpretability, although its predictive benefits vary across datasets. This study focuses on questionnaire-based depression severity estimation rather than the prediction of an independently established clinical diagnosis.