Applied Data Science Framework for Incremental and Interpretable Childhood Growth Risk Screening

Childhood growth monitoring plays an important role in the early identification of children who may experience developmental risks related to nutrition and health conditions. Conventional screening methods typically rely on anthropometric measurements that may not always be consistently obtained in community-based health environments. This study proposes an applied data science framework for incremental and interpretable screening of childhood growth risk using pose-derived body structure features combined with demographic and anthropometric attributes. Pose landmarks are extracted using the MediaPipe framework and integrated with variables including age, gender, and body weight to construct predictive models. Several machine learning algorithms are evaluated, including Logistic Regression, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and a Soft Voting Ensemble. Experimental evaluation using five-fold stratified cross-validation demonstrates that Logistic Regression achieves the highest predictive performance with a mean ROC-AUC of 0.901. Ablation analysis further indicates that incorporating pose-derived landmarks significantly improves classification performance compared with using demographic attributes alone. Interpretability analysis based on odds ratios highlights the contribution of pose features and demographic variables to prediction outcomes.