Construction of interpretable prediction model for lower limb deep vein thrombosis in patients undergoing total hip ar-throplasty based on machine learning and SHAP
Construction of interpretable prediction model for lower limb deep vein thrombosis in patients undergoing total hip ar-throplasty based on machine learning and SHAP
Objective To construct a machine learning model to predict the risk of deep venous thrombosis(DVT)in patients undergoing total hip arthroplasty(THA),and to identify key risk factors influencing DVT using shapley additive expla-nations(SHAP)method.Methods We retrospectively analyzed data from 416 patients who underwent THA in Wenzhou People's Hospital from January 1,2017 to July 31,2022,and randomly divided them into a training set and a test set in a 4:1 ratio.Recursive feature elimination and five-fold cross-validation were used to select the best features.Six machine learning al-gorithms were utilized to develop predictive models,and various performance metrics were employed to evaluate them.The SHAP method was used to analyze the interpretability of the optimal model.Results Four hundred and sixteen patients were included in the final study,including 333 in the training set and 83 in the test set.The XGBoost model was the most accurate on the test dataset,achieving a sensitivity of 0.817,specificity of 0.783,F1 score of 0.860,ROC-AUC of 0.800,and a Brier score of 0.106.SHAP summary plots showed that age,cholesterol,postoperative bed time,fibrinogen,and preoperative plasma D-dimer levels were the top five determinants for post-THA DVT.SHAP values feature dependence plots revealed complex non-linear effects of these factors on DVT risk,with age,bed rest,and fibrinogen showing an inverted U-shaped relationship,and cholester-ol displaying a positive correlation.Individual SHAP values offered insights into each predictor's role in DVT risk.Conclusion This study developed an efficient and interpretable machine learning model to predict DVT risk in THA patients,which is helpful for clinical health professionals in identifying high-risk patients and providing personalized intervention.
关键词
全髋关节置换术/深静脉血栓/机器学习/预测模型/模型解释
Key words
total hip arthroplasty/deep venous thrombosis/machine learning/predictive model/model interpretation
维普
万方数据