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In the recent past, Gestational Diabetes (GD) has become oneof the major health issues in the domain of healthcare. GD is a slow and silent killer that slowly influences the physical body resistance mechanism and fundamental organs. The GD is a harmful threat of health that had occurred during pregnancy of women by affecting metabolism of glucose levelwhich leadsto health issues of pregnancy women and infant, so it is most important of effective and early prediction of GD. In this paper, intelligent diagnosis and prediction of GD is proposed based on processing of various attributes of patient. In the data mining analysis, the Linear Discriminative Analysis (LDA) technique is applied as data normalization process on patients attributes for dimension reduction. The Modified Fuzzy C Means Clustering (MFCM) algorithm is used for clustering data after preprocessing. The enhanced Naïve Bayes classifier is applied for classifying various stages of GD of the patient. The experimental results prove that the proposed methodology is efficient and accurate in terms of graphical and numerical representations.
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