A New Intelligence Based Approach for Computer-Aided Diagnosis of Dengue Fever
This addresses the need for effective public health management and virological surveillance in diagnosing dengue fever, though it appears incremental as it builds on existing computational techniques.
The paper tackled the problem of diagnosing dengue fever early by developing a computational intelligence methodology to predict diagnosis in real-time, reducing false positives and negatives, and found it more accurate than state-of-the-art methods.
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.