基于基因表达数据的乳腺癌分期预测
Prediction of Breast Cancer Stage Based on Gene Expression Data

The stage of breast cancer determines its treatment and prognosis. Therefore, it is particularly significant to accurately locate the stage to which the patient belongs. This article aims to explore methods that can predict the stage of breast cancer through patients’ gene expression data. We obtained a balanced training data set by oversampling the data set and conducting random sampling with replacement on the late-stage samples with fewer data in order to select samples of the same size as the early samples. After that, we constructed a random forest model to predict the stage based on the balanced samples and achieved an accuracy of 96.75% with sensitivity 97.5% and specificity 89.3%. Then we compared the random forest model with kNN and SVM, the AUC values of the random forest model are higher than that of the other two methods. Ten-fold cross-validation was chosen to evaluate the random forest prediction model, and the average accuracy was 96.71%. The final result shows that the random forest model has impressive performance. After selecting the top 200 genes in importance according to the importance scores in random forest, we performed functional enrichment analysis. The pathways obtained by the enrichment were mostly related to breast cancer. It can be considered that the selected gene expression data are meaningful to predict the stage, so as to provide a certain basis for the treatment and prognosis of breast cancer in the future.