An ensemble learning model to predict lymph node metastasis in early gastric cancer

Lymph node metastasis is a critical factor for determining therapeutic strategies and assessing the prognosis of early gastric cancer. This work aimed to establish a more dependable predictive model for identify lymph node metastasis in early gastric cancer. The study utilized both univariate and multivariate logistic regression analyses to identify independent risk factors for lymph node metastasis of early gastric cancer, while employing five distinct algorithms to calculate feature weights. The optimal feature combination for each algorithm model was determined by combining the six highest weight features from all five models along with the independent risk factors. An ensemble learning model was subsequently constructed by integrating these five models. The model's performance was evaluated by the AUC, accuracy, and F1 score. Following this, a threshold was determined based on the F1 score, and the model's performance was assessed using an external validation set. The lymph node metastasis rate of early gastric cancer in our study was 16.4%. The ensemble learning model achieved an AUC value of 0.860 in the test set, with an accuracy of 82.35% and an F1 score of 0.611. Based on the F1 score, the model's threshold was set at 0.18. Additionally, the model demonstrated an AUC of 0.892 in the external validation set, along with an accuracy of 78.30% and an F1 score of 0.60.We constructed an ensemble learning model for predicting lymph node metastasis of early gastric cancer. Gastric surgery should be considered as the preferred treatment when the risk of lymph node metastasis exceeds 18%.