Esophageal squamous cell carcinoma risk prediction method based on clinical phenotype and logistic regression analysis

Abstract

The invention provides an esophageal squamous cell carcinoma risk prediction method based on clinical phenotype and logistic regression analysis. The method is used for realizing prognosis survival risk assessment of esophageal squamous cell carcinoma patients. The method comprises the following steps: firstly, screening out characteristic indexes according to clinical detection data of the esophageal squamous cell carcinoma patients, and constructing a decision tree classifier according to the characteristic indexes; secondly, dividing the esophageal squamous cell carcinoma patients into early-stage esophageal squamous cell carcinoma patients and middle-late-stage esophageal squamous cell carcinoma patients by utilizing the decision tree classifier; then, obtaining blood index informationof the esophageal squamous cell carcinoma patient one week before the operation, and screening out blood indexes with high correlation with the survival risk of the esophageal squamous cell carcinomapatient, and constructiung a logistic regression model; inputting the classified blood indexes of the esophageal squamous cell carcinoma patient into the logistic regression model to obtain a prognosis survival risk probability value of the esophageal squamous cell carcinoma patient; and judging the prognosis survival risk. According to the method, the postoperative survival state of the esophageal squamous cell carcinoma patient can be accurately judged, the risk prediction performance is improved, and the risk prediction cost is reduced.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a risk prediction method for esophageal squamous cell carcinoma based on clinical phenotype and logistic regression analysis. Background technique [0002] With the gradual increase in the incidence of cancer, model-based prediction of cancer prognosis has been widely used in different diseases, and accurate prognosis of cancer patients is still the most important problem. The clinically detected data is characterized by typical multicollinearity, high dimensionality, and much noise, which makes the data itself have problems such as information redundancy and nonlinearity, especially the characteristics of "high-dimensional" data have always been a major factor affecting data mining. On the one hand, the "high dimensionality" makes the processing of data require high computing costs, and on the other hand, the data itself cannot directly reflect the essential attributes....

AI Technical Summary

Problems solved by technology

[0004] In view of the deficiencies in the above-mentioned background technology, the present invention proposes a risk prediction method for esophageal squamous cell carcinoma based on clinical phenotype and logistic regression analysis, which solves the technical problem of low recognition rate caused by incomplete feature screening of existing prediction models

Images