Diabetes is a major health problem, the society faces today. Diagnosis and treatment of diabetes will improve the quality of life of affected individuals. Clinical Decision Support System (CDSS) serves as an aid for junior clinicians to diagnose diabetes in the absence of an expert diabetologist. This research aims to develop a CDSS diagnose the presence or absence of Gestational Diabetes Mellitus (GDM). The framework used to develop the CDSS has three subsystems, namely preprocessing subsystem, training subsystem and classification subsystem. Noisy values are handled by the preprocessing subsystem. The training subsystem fuzzifies the preprocessed data and constructs the hidden nodes of the RBFNN using the Gaussian Membership Function. The exact interpolation property of the Radial Basis Function Neural Network (RBFNN) is used to extract the weights between the hidden layer and the output layer. The extracted weights are used to prune the generated fuzzy rules. Finally, the pruned rules are stored in a knowledge base. The Fuzzy Inference System uses the rules from the knowledgebase to classify the samples in the testing set. The CDSS for Gestational Diabetes Mellitus attains an overall accuracy of 88.31% with 79.31% sensitivity and 93.75% specificity. Our CDSS yields comparable classification performance when compared to the researchs of other researchers in the past decade. The CDSS serves as a second source of opinion for junior clinicians for the diagnosis of GDM. The classification frameworks used in this CDSS can be adopted for other clinical datasets. © Medwell Journals, 2016.