Dengue viruses are members of the Flaviviridae, transmitted principally in a cycle involving humans and mosquito vectors. A severe form, dengue hemorrhagic fever (DHF), is an immune pathologic disease occurring in persons who experience sequential dengue infections. Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever (DF) in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. we develop in this paper a new computational intelligence-based methodology that predicts the diagnosis in real time, minimizing the number of false positives and false negatives. In Our methodology consists a novel missing value imputation procedure that can be applied on any dataset, a wrapper-based feature selection method for extracting attribute that can diagnose the illness. The predictive models developed using our methodology is found to be more accurate than the state-of-the art methodologies used in the diagnosis of the DF.