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AIM <p>To develop and evaluate machine learning (ML) models for early cerebral palsy (CP) prediction and identify synergistic perinatal risk factors in a pediatric population.</p> Method <p>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.</p> Results <p>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; <i>p</i> &lt; 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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Foysal Ahammad,
  • Munira Aden,
  • Ayesha Banu,
  • Muhammad Marwan,
  • Sadam Hussain,
  • Safa Salim,
  • Adnan Mohammed,
  • Tanvir Alam,
  • Lisa Thornton,
  • Farhan Mohammad

摘要

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.