IT3FLS-BASED ONTOLOGICAL FRAMEWORK FOR ASSESSING THE SUCCESS RATE OF PONSETI CASTING IN CLUBFOOT TREATMENT

Abstract

Clubfoot represents a congenital deformity of the musculoskeletal system where the foot twists inward, often impairing gait and overall mobility if left uncorrected. Among available corrective methods, the Ponseti approach remains the gold standard for non-surgical treatment, employing systematic manipulation, sequential casting, and post-correction bracing to sustain alignment. In this study, a dataset comprising 3000 patient cases and 39 clinical, demographic, and functional features was analyzed to explore determinants influencing treatment success. Ontology-based data modeling was incorporated to enrich semantic understanding and ensure structured representation of clinical knowledge. The findings identified age at initial casting, affection type, and gender as primary factors shaping treatment response. Baseline functional scores (MFCS and HFCS) averaged between 2.25 and 2.5, indicating variations across individual cases. Principal Component Analysis underscored these variables as major contributors to outcome diversity. Employing the Interval Type-3 Fuzzy Logic System (IT3FLS) yielded accurate predictive results (RMSE = 0.3414; MSE = 0.1165; MAE = 0.3066; MAPE = 30.66%), confirming its robustness for clinical analytics and outcome evaluation in Ponseti casting.