Evaluation of Logistic Regression Model with Feature Selection Methods on Medical Dataset

gression enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome or response can be either dichotomous (yes, no) or ordinal (low, medium, high). During dichotomous response, we are performing standard logistic regression and for ordinal response, model that uses standard logistic regression formula with feature selection using forward selection and backward elimination methods and has been evaluated for the effectiveness of the results on publicly available medical datasets. The process of evaluation is as follows. The feature selection algorithm using forward selection and backward elimination method is applied on the dataset and the selected features performance of the predictive model. From the experimental results it is observed that logistic regression model with feature selection using forward selection and backward elimination methods gives more reliable result than the logistic regression model.