Multicollinearity detection and feature selection in diagnosis of cardio related tests

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Dr.L.V.Nandakishore, Dr.S.Aruna


 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.

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