Machine learning driven modeling of synergistic perinatal risk profiles in early onset pediatric cerebral palsy.

AIM: To develop and evaluate machine learning (ML) models for early cerebral palsy (CP) prediction and identify synergistic perinatal risk factors in a pediatric population. METHOD: We conducted a retrospective case-control study using demographic, perinatal, and postnatal clinical data collected at Sidra Medicine, Qatar. Four ML models- Random Forest (RF), XGBoost, Support Vector Machine (SVM), and a feedforward neural network (FFN) were trained using clinically relevant features. Model performance was assessed using precision, recall, area under the curve (AUC), F1-score, and SHAP-based interpretability. A multidimensional interaction framework was used to evaluate cumulative risk across 16 subgroups. RESULTS: All ML models exhibited high predictive accuracy (ROC-AUC: 0.98-0.99, and PR-AUC: 0.97-0.98), with four key factors: low birth weight (LBW), premature birth, neonatal intensive care unit (NICU) admission, and multiple pregnancies. Infants exposed to all four factors demonstrated a 93.15% incidence of CP (OR = 1382.67; p < 0.0001). A clear dose-response gradient was observed across exposure subgroups. SHAP analysis confirmed consistent cross-model importance of LBW, very preterm birth, NICU admission, and multigravidity. Cross-validation confirmed model robustness, and severity analysis identified NICU admission and birth weight as independent predictors of higher GMFCS classification. CONCLUSION: Four ML models achieved high predictive accuracy (ROC-AUC 0.98-0.99) for CP risk stratification in a Middle Eastern pediatric cohort, with LBW, very preterm birth, NICU admission, and multigravidity as the most consistent cross-model predictors. The synergistic interaction of these exposures - evidenced by a 93.15% CP incidence in the highest-risk subgroup - supports a paradigm shift from single-factor screening to exposure-weighted, ML-driven neonatal surveillance. These findings provide a data-driven foundation for early risk stratification and targeted intervention planning in high-risk neonatal population.