Development of a prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty: an automated machine learning approach

Objective To develop an automated machine learning (AutoML)-based prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty (ACCR), addressing the clinical challenges of postoperative complications and satisfaction disparity. Methods A retrospective cohort of 447 ACCR patients (2019–2024) was analyzed, integrating 20+ parameters spanning biological, surgical, and behavioral domains. We proposed an improved metaheuristic algorithm (INPDOA) for AutoML optimization, validated against 12 CEC2022 benchmark functions. Bidirectional feature engineering identified critical predictors, and SHAP values quantified variable contributions. A MATLAB-based clinical decision support system (CDSS) was developed for real-time prognosis visualization. Results The INPDOA-enhanced AutoML model outperformed traditional algorithms, achieving a test-set AUC of 0.867 for 1-month complications and R 2 = 0.862 for 1-year Rhinoplasty Outcome Evaluation (ROE) scores. Key predictors included nasal collision within 1 month, smoking, and preoperative ROE scores. Decision curve analysis demonstrated a net benefit improvement over conventional methods. The CDSS reduced prediction latency. Conclusion This study establishes the first AutoML-driven prognostic framework for ACCR, effectively bridging the gap between surgical precision and patient-reported outcomes. Its integration of dynamic risk prediction and explainable AI offers a paradigm for aesthetic surgical decision-making.