Fa yi xue za zhi · 2026
[Machine Learning Model Based on Structured Injury Features for Knee Dysfunction after Traumatic Injury].
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Dou Run-Ting, Cheng Shun, Zhou Xin, Ye Xing, Wang Zhi-Min, Hong Guang-Hui, Zhang Qi, Xia Qing, Shen Yi-Wen
Аннотация
OBJECTIVES: To extract structured injury features of knee trauma from forensic case files, and to assess knee functional impairment using a machine learning model combined with the voting method.
METHODS: A total of 490 forensic cases involving knee trauma were retrospectively collected and randomly divided into training and testing sets at an 8:2 ratio. Structured injury features were extracted and systematically organized and stored using a MySQL database. Six machine learning models, including support vector classification, random forest, logistic regression, gradient boosting, k-nearest neighbor, and extreme gradient boosting, were applied to select the optimal models. Using a 25% loss of joint range of motion as the threshold, a model for classifying the severity of knee functional impairment was established by combining the selected models with a voting method. The best models were first selected based on their average AUC values, and further validated using 5-fold cross-validation. The SHAP method was used to analyze and interpret the prediction results of the optimal model. In addition, 57 similar cases were collected as an external validation to evaluate the model's generalization ability.
RESULTS: The average AUC values for support vector machine, random forest, and extreme gradient boosting all exceeded 0.9. In 5-fold cross-validation, each of the three individual models achieved an average AUC value of 0.89. After integrating these three models using the voting method, the average AUC of 5-fold cross-validation increased to 0.91. The model's performance, and the evaluation metrics on the external validation set were comparable to those from internal validation.
CONCLUSIONS: The developed machine learning model based on structured injury features demonstrates good performance in classifying the severity of motor dysfunction following knee trauma, with high model interpretability and strong generalization capability.
METHODS: A total of 490 forensic cases involving knee trauma were retrospectively collected and randomly divided into training and testing sets at an 8:2 ratio. Structured injury features were extracted and systematically organized and stored using a MySQL database. Six machine learning models, including support vector classification, random forest, logistic regression, gradient boosting, k-nearest neighbor, and extreme gradient boosting, were applied to select the optimal models. Using a 25% loss of joint range of motion as the threshold, a model for classifying the severity of knee functional impairment was established by combining the selected models with a voting method. The best models were first selected based on their average AUC values, and further validated using 5-fold cross-validation. The SHAP method was used to analyze and interpret the prediction results of the optimal model. In addition, 57 similar cases were collected as an external validation to evaluate the model's generalization ability.
RESULTS: The average AUC values for support vector machine, random forest, and extreme gradient boosting all exceeded 0.9. In 5-fold cross-validation, each of the three individual models achieved an average AUC value of 0.89. After integrating these three models using the voting method, the average AUC of 5-fold cross-validation increased to 0.91. The model's performance, and the evaluation metrics on the external validation set were comparable to those from internal validation.
CONCLUSIONS: The developed machine learning model based on structured injury features demonstrates good performance in classifying the severity of motor dysfunction following knee trauma, with high model interpretability and strong generalization capability.