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.

Fuzzy Logistic Regression 模型可以在数据集由于模糊性而包含歧义且不能考虑犹豫程度时估计参数。因此，本文提出了一种新颖的直觉模糊逻辑回归模型来处理一次包含模糊度和犹豫度的不精确参数。在这种情况下，使用修正的田中模型来估计参数。为了说明所提出的直觉模糊逻辑回归的工作效率，我们提出了出生体重数据集并应用它。比较研究也与其他统计模型一起进行。模型的适用性和有效性通过使用隶属函数的平均度获得拟合优度标准来揭示。在应用于出生体重数据集时，观察到直觉模糊逻辑回归值表明拟合良好，被认为是最适合婴儿出生体重数据集的直觉模糊逻辑回归模型。使用所提出的直觉模糊逻辑回归的平均隶属度值相对更显着，表明该模型与模糊逻辑回归模型相比具有最佳拟合度。