Using Stepwise Method to Find the Most Influencing Feature to the Cell Nuclei of a Breast Mass

Abstract

In real life, the influencing factors that lead to an outcome are varied and complex. However, how to organize the complex variable relationship into a concise and effective model for prediction is critical. Therefore, the Stepwise method was used in this study to organize the relationship between malignant and benign detection of breast cancer tumors and changes in cell characteristics into a model. In this paper, the diagnosis data of breast cancer of patients in the hospital were quoted. The benign and malignant characteristics of breast cancer were taken as independent variables, and the characteristic changes of cells were taken as dependent variables to conduct data analysis with R language, so as to obtain the most effective model. The results show that the prediction accuracy of the model obtained by AIC method is the highest. AIC selected seven dependent variables and reached 79.8 percent. The model prediction accuracy of BIC was 77.2 percent. Compared with AIC method, BIC only selected four dependent variables and obtained a more concise model. However, BIC is too concise and loses the accuracy of model prediction in some aspects.