Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree.

ABSTRACT: The diagnosis of the degree of differentiation of tumor cells can help physicians to make timely detection and take appropriate treatment for the patient's condition. In this study, the original dataset is clustered into two independent types by the Kohonen clustering algorithm. One type is used as the development sets to find correlation indicators and establish predictive models of differentiation, while the other type is used as the validation sets to test the correlation indicators and models. In the development sets, thirteen indicators significantly associated with the degree of differentiation of esophageal squamous cell carcinoma are found by the Kohonen clustering algorithm. Thirteen relevant indicators are used as input features and the degree of tumor differentiations is used as

SUBMITTER: Wang Y 

PROVIDER: S-EPMC7645151 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature