Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures

Abstract

Abstract Deep vein thrombosis (DVT) is a common complication in patients with lower extremity fractures. Once it occurs, it will seriously affect the quality of life and postoperative recovery of patients. Therefore, early prediction and prevention of DVT can effectively improve the prognosis of patients. Based on the predictive factors of DVT in patients with lower limb fractures, this study constructed a DVT prediction model with the help of different machine learning classification models to explore the effectiveness of different models in predicting DVT. The researchers conducted a retrospective analysis of DVT-related factors in patients with lower limb fractures from the Second Affiliated Hospital of Nanchang University from July 2017 to July 2023, and then calculated the incidence of DVT. Five prediction models were applied to the experiment, including Extreme Gradient Boosting (XGBoost) model, Logistic Regression (LR) model, RandomForest (RF) model, Multilayer Perceptron (MLP) model and Support Vector Machine(SVM) model. Afterwards, the performance of the obtained prediction models were evaluated by area under the curve (AUC), accuracy, sensitivity, specificity and F1 score. A total of 4,424 patients were included in this study, of which 207 patients had DVT. Theincidence rate of DVT was 4.68%. The prediction performance of the model based on machine learning: XGBoost model (AUC=0.730, accuracy=0.951), LR model (AUC =0.740, accuracy=0.712), RF model (AUC=0.703, accuracy=0.952), MLP model (AUC=0.571, accuracy=0.704), SVM model (AUC=0.488, accuracy=0.826). Although the LR model has the largest AUC, its accuracy is not as good as that of the XGBoost model. By comparing the AUC and accuracy, the XGBoost model performed the best. The DVT prediction model constructed by the XGB has high reproducibility, universality and feasibility. However, the model still needs external verification research before clinical application.