Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning

Abstract Objectives The aim of this study is to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide ability for identifying the risk of NLM recurrence and guidance of clinical treatment plan. Methods This study was conducted on inpatients who were admitted to the Mammary Department of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine between July 2019 to December 2021. Inpatients data following up has been completed until December 2022. We used two ML approaches (RF and XGBoost) to build models and predict the NLM recurrence risk of female patients. Totally 258 patients have been randomly divided into a training set and a test set according to 75%-25% proportion. The model performance was evaluated based on Accuracy, Precision, Recall, F1-score, AUC. The Shapley Additive Explanations (SHAP) method was used to interpret the model. Results There were 48 (18.6%) NLM patients who experienced recurrence during the follow-up period. Ten features were selected in this study to build the ML model. For the RF model, BMI is the most important influence factor and for the XGBoost model is intraoperative discharge. The results of tenfold cross-validation suggest that both RF model and XGBoost model have good predictive performance, but XGBoost model has a better performance than RF model in our study. The trends of SHAP values of all features in our models are consistent with the trends of these features’ clinical presentation. The inclusion of these ten features in the model is necessary to build practical prediction models for recurrence. Conclusions The results of tenfold cross-validation and SHAP values suggest that the models have predictive ability. The trend of SHAP value provides auxiliary validation in our models and makes it has more clinical significance.