Machine learning to build prognostic signatures
REGRESSION
In the R(4.2.1) environment, a total of 10 machine learning algorithms, including random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression (GBM), and survival support vector machine (survival-SVM) were used. In the process, we used one algorithm to filter the variables and another algorithm to build the prognostic signature. When the final prognostic signature contained less than 5 genes, the signature was considered an invalid signature. A total of 98 combinations of machine learning algorithms were eventually integrated. Finally, Harrell’s concordance index (C-index) was calculated for each signature, and the signature with the highest average C-index value was considered to be the best signature. After calculating the risk score for each patient using the predict function, the optimal cutoff value for the risk score is determined using the surv_cutpoint function in the "srvminer" package. Based on the optimal cutoff value of the risk score, patients are divided into high-risk and low-risk groups.