Prognostic assessment and intelligent prediction system for breast reduction surgery using improved swarm...

Area of Science:

• Plastic Surgery
• Artificial Intelligence
• Swarm Intelligence Optimization

Background:

• Accurate prognosis assessment is crucial for reduction mammaplasty.
• Existing methods may lack precision in predicting postoperative outcomes.

Purpose of the Study:

• To enhance reduction mammaplasty prognosis accuracy using an improved swarm intelligence algorithm.
• To develop an intelligent prediction system for clinical decision support.

Main Methods:

• An Improved Secretary Bird Optimization Algorithm (ISBOA) was developed and integrated with an AutoML framework.
• Classification and regression models were used to predict postoperative complications and patient satisfaction.
• Data from 453 reduction mammaplasty patients were analyzed.

Main Results:

• The ISBOA algorithm demonstrated superior stability and convergence speed.
• The AutoML model achieved high accuracy in predicting complications (ROC-AUC: 0.9369) and quality of life (R²: 0.9165).
• SHAP and DCA analyses validated the model's predictive power and clinical utility.

Conclusions:

• An effective prognosis assessment and prediction system for reduction mammaplasty was established.
• The ISBOA algorithm and AutoML model offer significant advantages for predicting patient outcomes.
• The developed system enhances the scientific rigor and efficiency of clinical decision-making in plastic surgery.