A New Intelligence Based Approach for Computer-Aided Diagnosis of Dengue Fever

TL;DR

This paper introduces a novel computational approach combining missing data imputation, feature selection, and boosting decision trees to improve early diagnosis accuracy of dengue fever, aiding public health efforts.

Contribution

The paper presents a new integrated methodology that enhances dengue diagnosis accuracy through innovative data handling and machine learning techniques.

Findings

Higher diagnostic accuracy than existing methods

Effective identification of influential symptoms

Real-time prediction capability

Abstract

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.