Lasso-based esophageal squamous cell carcinoma patient risk prediction column diagram model establishment method

Abstract

The invention provides a Lasso-based esophageal squamous cell carcinoma patient risk prediction column diagram model establishment method, which is used for evaluating the postoperative survival risk of esophageal squamous cell carcinoma patients. The method comprises the following steps: firstly, collecting clinical data of esophageal squamous cell carcinoma patients, analyzing the clinical data by utilizing single-factor Cox, Lasso and multi-factor Cox regression analysis methods to obtain important characteristic variables, and establishing probability prediction models with different characteristic dimensions; secondly, selecting a probability prediction model with better performance and establishing a postoperative risk prediction column diagram model of the esophageal squamous cell carcinoma patient; and finally, dividing the patients into a high-risk group and a low-risk group according to the postoperative risk prediction column diagram model of the esophageal squamous cell carcinoma patients, and verifying the reliability and effectiveness of model classification through a KM survival curve analysis method. According to the method, the postoperative survival risk of the esophageal squamous cell carcinoma patient can be accurately predicted, reference is better provided for treatment of the esophageal squamous cell carcinoma patient, and meanwhile the risk prediction cost is reduced.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a Lasso-based method for establishing a nomogram model for risk prediction of patients with esophageal squamous cell carcinoma. Background technique [0002] The risk prediction model to evaluate the prognosis of patients has been widely used in different diseases. In China, the incidence of esophageal squamous cell carcinoma is relatively high. Early detection and effective treatment of esophageal squamous cell carcinoma have always been concerned by experts and scholars. Accurate prognosis remains a major challenge. The occurrence of esophageal squamous cell carcinoma is not the result of a single factor. The clinical data collected from patients with esophageal squamous cell carcinoma has the characteristics of redundant information and noise. Current clinical medical methods cannot completely improve the prognosis of patients, but by mining clinical detection data E...

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Problems solved by technology

[0004] In view of the deficiencies in the above-mentioned background technology, the present invention proposes a Lasso-based method for establishing a nomogram model for risk prediction of patients with esophageal squamous cell carcinoma, which solves the unreliability of the existing prediction model due to too many or too few feature selections. , technical problems with low recognition rate

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