Design and implementation of cancer patient survival prediction model based on ensemble learning method

To study the impact of genomics on cancer diseases, bioinformatics and integrated learning methods are used to conduct survival analysis on colon cancer and rectal cancer data in the cancer gene map database. Firstly, according to the significant expression and stability test, the long-chain non-coding RNA in the transcriptome that has a significant impact on clinical prognosis survival analysis was initially screened. Then use a random forest ensemble learning algorithm to train it to get a preliminary model. Finally, based on the optimized random survival forest model, the Cox regression model was once again integrated, and the risk values of the two were integrated. The RCCT (Random-Cox Combined to Survival) method was proposed to provide clinical decision-makers certain reference values.