| Summary: | The Fuzzy Logistic Regression model can estimate the parameters when the data-set contains ambiguousness due to vagueness and cannot consider the degree of hesitation. Thus, this paper proposes a novel Intuitionistic Fuzzy Logistic Regression model to deal with the imprecise parameters containing the vagueness and hesitation degrees at a time. In this context, the revised Tanaka’s model is used to estimate the parameters. To illustrate the working efficiency of the proposed intuitionistic fuzzy logistic regression, we have presented the birth-weight data-set and applied it. The comparative study is also performed with other statistical models. The applicability and validity of the model are revealed by obtaining the goodness of fit criteria using Mean degrees of membership functions. On applying to the birth-weight data-set, it is observed that the intuitionistic fuzzy logistic regression values signify the good fit and are considered as the best fitting intuitionistic fuzzy logistic regression model for babies’ birth-weight data-set. The value of mean degree of membership is relatively more significant by using the proposed intuitionistic fuzzy logistic regression, which indicates the best fitting of the model compared to the fuzzy logistic regression model.
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