  Identification of the influential clinical symptoms and laboratory features
that help in the diagnosis of dengue fever in early phase of the illness would
aid in designing effective public health management and virological
surveillance strategies. Keeping this as our main objective 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. Our methodology consists of three major components (i) a novel
missing value imputation procedure that can be applied on any data set
consisting of categorical (nominal) and/or numeric (real or integer) (ii) a
wrapper based features selection method with genetic search for extracting a
subset of most influential symptoms that can diagnose the illness and (iii) an
alternating decision tree method that employs boosting for generating highly
accurate decision rules. The predictive models developed using our methodology
are found to be more accurate than the state-of-the-art methodologies used in
the diagnosis of the dengue fever.
