Developing a comprehensive risk prediction model for the emergence of lower extremity lymphedema following gynecological cancer surgery by harnessing the power of integrative multi-factor analysis and cutting-edge machine learning algorithms

Abstract Objective Establish the predictive efficacy of postoperative lower extremity lymphedema in gynecological malignant tumors to precisely identify high-risk individuals and enable timely, proactive interventions. Methods A comprehensive retrospective analysis was conducted on the clinical records of 712 patients with gynecological malignant tumors who underwent surgical intervention at a tertiary hospital in China between January 2016 and January 2021. The postoperative disease trajectories were meticulously documented and evaluated. Based on the development of lower extremity lymphedema, patients were stratified into an occurrence group (n = 121) and a non-occurrence group (n = 591). Data were randomly allocated into training and validation sets using a 7:3 partition ratio, with the latter comprising 30% of the total cohort. The training set served as the foundation for model development, while the validation set ensured rigorous assessment of predictive performance. Through logistic regression analysis, a refined set of significant predictors was identified. Subsequently, a robust risk prediction model for post-surgical lower extremity lymphedema was constructed by integrating traditional logistic regression with five advanced machine learning algorithms—random forest, support vector machine, gradient boosting machine, neural network, and extreme gradient boosting—thereby harnessing both statistical rigor and computational intelligence to enhance predictive accuracy. Results Univariate logistic analysis unveiled that tumor type, FIGO stage, pathological grade, number of pelvic lymph node dissections, postoperative radiotherapy, hypertension, and prolonged postoperative daily standing time were significantly associated with an elevated risk of lower extremity lymphedema in patients with gynecological malignancies, whereas increased postoperative daily exercise duration emerged as a protective determinant. Multivariate logistic regression further identified hypertension, advanced FIGO stage, extensive pelvic lymph node resection, postoperative radiotherapy, and prolonged standing time as independent predictors of disease onset. Leveraging traditional logistic regression alongside five widely adopted machine learning algorithms, a comprehensive predictive model was meticulously constructed and rigorously evaluated. Through systematic screening across three critical dimensions—discriminative accuracy, calibration fidelity, and clinical utility—the gradient boosting machine (GBM) demonstrated superior performance, outperforming all competing models. SHapley Additive exPlanations (SHAP) analysis elucidated the hierarchical importance of predictive features: FIGO stage ranked highest in influence, followed by postoperative daily standing time, number of pelvic lymph node resections, and postoperative radiotherapy, with hypertension contributing the least. Finally, through in-depth feature effect profiling, dependency structure exploration, and individualized prediction interpretation, the model’s behavioral patterns were validated to align seamlessly with the findings of multivariate logistic regression, affirming both its statistical consistency and clinical interpretability. Conclusion The gradient boosting machine (GBM) prediction model, meticulously constructed by integrating five pivotal factors—hypertension, FIGO stage, extent of pelvic lymph node resection, postoperative radiotherapy, and daily standing duration following surgery—demonstrates robust predictive performance in forecasting the onset of lower extremity lymphedema after gynecological malignant tumor surgery. Clinically, this evidence-based risk assessment tool empowers healthcare providers to stratify patient risk with greater precision, offering a valuable framework for clinical consultation, identification of high-risk cohorts, and personalized follow-up strategies, thereby facilitating early intervention and optimized therapeutic decision-making.