The Diagnosis of Dengue Disease: An Evaluation of Three Machine Learning Approaches

Shalini Gambhir, Sanjay Kumar Malik and Yugal Kumar
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Shalini Gambhir: SRM University, Sonepat, Haryana, India
Sanjay Kumar Malik: SRM University, Sonepat, India
Yugal Kumar: Jaypee University of Information Technology, Solan, India

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2018, vol. 13, issue 3, 1-19

Abstract: This article describes how Dengue fever is a fatal and hazardous disease resulting from the bite of several species of the female mosquito (principally, Aedesaegypti). Symptoms of the dengue fever mimic those of a number of other infectious and/or mosquito-borne tropical diseases such as Viral flu, Chikungunya, and Zika fever. Yet, with dengue fever, human life can be more at risk due to severe depletion of blood platelets. Thus, early detection of dengue disease can ensure saving lives; furthermore, it can help in making a preventive move before the disease progresses to epidemic proportion. Hence, the target of this article is to propose a model for an early detection and precise diagnosis of dengue disease. In this article, three prevalent machine learning methodologies, including, Artificial Neural Network (ANN), Decision Tree (DT) and Naive Bayes (NB) are evaluated for designing a diagnostic model. The performance of these models is assessed utilizing available dengue datasets. Results comparing and contrasting performance of diagnostic models utilizing accuracy, sensitivity, specificity and error rate parameters showed that ANN-based diagnostic model appears to yield better performance measures over both the DT and NB models.

Date: 2018
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