Multicollinearity detection and feature selection in diagnosis of cardio related tests

Abstract

 In this paper, data set relating to the results of the various diagnostic tests done on patients to diagnose cardiac related problems with variables are considered. A linear regression equation is found with class as a response variable and the other variables as continuous predictors. The same was analyzed for multicollinearity by calculating the variance inflation factor (VIF). To get a better regression equation with no multicollinearity, the variables with high VIF values were eliminated. Feature reduction was applied to the data, and the outcomes were compared.