Discussion
breast cancer
RECURRENCE, BREAST CANCER, EVENT, REGRESSION, EVENTS
In this study, we attempted to develop a machine learning model based on an RF algorithm for predicting the occurrence of events among BC patients. And we identified that compared to the logistic regression, the RF model could reach a satisfactory performance to predict the events occurring of BC patients post-treatment.RF was first introduced in the early 2000 s. Based on Breiman’s “bagging” idea and the random selection of features, RF could obtain higher accuracy. Cutler has concluded that compared to other classifiers, RF has the advantage of (1) high classification accuracy; (2) identifying variable importance; (3) the ability to perform multiple types of data analysis; (4) modeling complex interactions between explanatory variables [Based on previous studies, we retrospectively analyzed the data of BC patients in our hospital and selected 9 variables for comparison among whether patients had an event or not. Then, we included all of these 9 variables for establishing the model. However, the model did not perform very well, which was with a relatively higher OOB error rate and a lower AUC. The importance analysis also showed that only HER2 was significant in the model and almost half of these variables were negatively associated with the outcome variable. Hence, we tried to decrease the number of explanatory variables. After conducting multiple comparisons of variable combinations, we ultimately discovered that in the 1-year cohort, the model exhibited lower error rates and higher performance when utilizing 5 variables. Thus, we used Stage T, stage N, HR, HER2, and P53 as explanatory variables to predict the event. Similarly, in the 5-year cohort, we found that when the number of variables was 3, the model would do better, and we used cT, HR, and HER2 as explanatory variables to predict the even.For machine learning, interpretability of models is a somewhat subjective quality that cannot be formally defined through rigorous mathematical expressions. In our view, as a general rule, there tends to be a positive correlation between a model's complexity and its accuracy, with greater complexity often leading to reduced interpretability. Indeed, in our study, we couldn’t explain why, in the random forest model, selecting these specific independent variables leads to better predictive performance. However, we cannot abandon the model solely due to its low interpretability.There were limitations in our work. First, only a part of the bio-data was selected for analysis. Other information such as blood analysis data, CT or MRI imaging data, and pathological imaging data were omitted. Second, we only developed 2 kinds of machine learning models. Third, although we have obtained some valuable conclusions, there were no new findings in Cox regression. Fourth, in the first year, there were only 30 patients occurring event, The data imbalance might indeed affect the model development. The most importantly, we used statistical methods to reduce the representative features for training the random forest model. These might affect the performance of the model. However, through this study, we have demonstrated that machine learning models can achieve promising results in accurately predicting events occurring based on simple clinical variables. In the future, it is of vital importance to supplement the data, such as imaging and pathological images. To extract image variables using deep learning and then construct a model based on these data.Nevertheless, this study confirmed the effectiveness of the RF model in predicting events in breast cancer patients.. In the future, the study should focus on multi-center research, supplementing the database, especially the imaging and pathological pictures. And improving the variable selecting method. We hope that our work can help clinical decision-making among patients at high risk for recurrence, timely warning, and timely intervention. Further research should include more patients in multiple clinical centers, select more explanatory variables, and base on multiple machine learning and deep learning algorithms to construct predicted models (Additional file 