Influence of different factors on survival of patients with colorectal cancer

TL;DR

This study analyzes risk and survival factors in colorectal cancer patients using statistical models, highlighting the effectiveness of principal component analysis combined with risk and regression models for data analysis and prediction.

Contribution

It introduces a combined approach of principal component analysis with competitive risk and linear regression models for analyzing and predicting colorectal cancer survival.

Findings

Principal component analysis reduces variable complexity effectively.

Combination of models improves data analysis accuracy.

Predictive models can estimate patient survival times.

Abstract

Colorectal cancer refers to the cancer from the dentate line to the junction of rectosigmoid colon, which is one of the most common malignant tumors of the digestive tract. The treatment of colorectal cancer is controversial, so understanding the risk factors and survival factors of colorectal cancer is of great significance for the diagnosis of patients. This study sampled patients with colorectal cancer from the SEER database. The factors affecting the survival of colorectal cancer patients were analysed by combining principal component analysis and competitive risk model, and the survival time of patients was analysed by combining principal component analysis and linear regression. Finally, the data were predicted. The results show that principal component analysis can effectively reduce the number of variables, and the combination of competitive risk model and linear regression model can effectively analyse and predict the data.