To develop a machine learning-based predictive model for cancer-related fatigue (CRF) risk in ovarian cancer patients.
Key Findings:
CRF prevalence among participants was 39.6%.
The SVM model achieved an AUC of 0.884, accuracy of 0.829, sensitivity of 0.816, specificity of 0.838, and F1 score of 0.792.
The model demonstrated good calibration with a Brier score of 0.132.
Decision curve analysis indicated the highest net benefit across a range of threshold probabilities (0.05–0.85).
Key predictive features included serum calcium level, anxiety-depression status, red blood cell count, education level, cancer stage, medical payment method, and marital status.
Interpretation:
The SVM model exhibits robust predictive efficacy and good clinical utility for CRF risk stratification in ovarian cancer care.
Limitations:
Conclusion:
The SVM model serves as a valuable tool for early identification of high-risk patients, enabling targeted interventions to improve outcomes.
