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Predicting lower limb lymphedema after cervical cancer surgery using artificial neural network and decision tree
Jin Hu1, Gaoming Liu1, Yuanyuan Liu1
1Department of Lymphedema Rehabilitation, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
Purpose:
This study aimed to develop and validate accessible artificial neural network and decision tree models to predict the risk of lower limb lymphedema after cervical cancer surgery.
Methods:
We selected 759 patients who underwent cervical cancer surgery at the Hunan Cancer Hospital from January 2010 to January 2020, collecting demographic, behavioral, clinicopathological, and disease-related data. The artificial neural network and decision tree techniques were used to construct prediction models for lower limb lymphedema after cervical cancer surgery. Then, the models' predictive efficacies were evaluated to select the optimal model using several methods, such as the area under the receiver operating characteristic curve and accuracy, sensitivity, and specificity tests.
Results:
In the training set, the artificial neural network and decision tree model accuracies for predicting lower limb lymphedema after cervical cancer surgery were 99.80% and 88.14%, and the sensitivities 99.50% and 74.01%, respectively; the specificities were 100% and 95.20%, respectively. The area under the receiver operating characteristic curve was 1.00 for the artificial neural network and 0.92 for the decision tree model. In the test set, the artificial neural network and decision tree models' accuracies were 86.70% and 82.02%, and the sensitivities 65.70% and 67.11%, respectively; the specificities were 96.00% and 89.47%, respectively.
Conclusion:
Both models had good predictive efficacy for lower limb lymphedema after cervical cancer surgery. However, the predictive performance and stability were superior in the artificial neural network model than in the decision tree model.
