Establishment and comparison of three fear of progression risk prediction models for gynecological malignancies patients based on machine learning

The Random Forest-based prediction model exhibited optimal performance, demonstrating high accuracy and reliability in identifying FoP risk among gynecological malignancy patients. It can provide a scientific foundation for early FoP detection and personalized intervention strategies. These findings underscore the clinical utility of combining machine learning approaches with social-ecological theory to advance precision nursing practices in psycho-oncology care.