An Ontology Driven Machine Learning Framework for Early Prediction in Children with Cerebral Palsy

Cerebral palsy (CP) is a neurological disorder that affects 2-3 in every 1,000 births worldwide. Early prediction of severity is vital for optimizing therapeutic interventions. This study introduces OntoML-CP, a novel hybrid intelligence framework that combines inductive machine learning with deductive ontology-based reasoning to predict Gross Motor Function Classification System (GMFCS) levels in children with CP. We present a hybrid architecture combining semantic features from a CP ontology and clinical data for machine learning, using ontological reasoning to refine predictions and improve clinical validity and interpretability. The clinical ontology built using OWL captures the relationships between symptoms of cerebral palsy, developmental disorders, and motor functions, enriched with clinical knowledge and FOAF to represent key stakeholders like patients, parents, and therapists. Using a synthetic dataset of 1,695 children with CP, generated by physical medicine and rehabilitation specialists based on real clinical cases and validated through expert review, we address demographic diversity and missing data through preprocessing techniques to correct class imbalance during model evaluation and selection. Seven supervised algorithms were evaluated, among which Random Forest and Gradient Boosting models achieved superior performance (accuracy: 85% and 83%), when augmented with our ontological framework. The models showed consistent performance across all GMFCS levels with macro-averaged F1-scores of 0.81 and 0.79, respectively, and maintained high sensitivity for severe cases (levels 4-5), significantly outperforming baseline models. The semantic layer enhances predictions with logical explanations and presents them through SPARQL queries and intuitive visual formats designed for healthcare professionals. Our ontology-driven approach provides medicine with not only accurate predictions but also context-aware, clinically interpretable explanations that support informed decisions and enable personalized, actionable CP severity predictions.