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

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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)

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.

Suggested Citation

  • Shalini Gambhir & Sanjay Kumar Malik & Yugal Kumar, 2018. "The Diagnosis of Dengue Disease: An Evaluation of Three Machine Learning Approaches," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 13(3), pages 1-19, July.
  • Handle: RePEc:igg:jhisi0:v:13:y:2018:i:3:p:1-19
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    Cited by:

    1. Naizhuo Zhao & Katia Charland & Mabel Carabali & Elaine O Nsoesie & Mathieu Maheu-Giroux & Erin Rees & Mengru Yuan & Cesar Garcia Balaguera & Gloria Jaramillo Ramirez & Kate Zinszer, 2020. "Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(9), pages 1-16, September.
    2. Kai-Xiang Zhuang & I-Ching Hsu, 2020. "Knowledge Fusion Based on Cloud Computing Environment for Long-Term Care," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 15(4), pages 38-55, October.
    3. Supreet Kaur & Sandeep Sharma & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry & Muhammad Shafiq & Salil Bharany, 2022. "Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System," Sustainability, MDPI, vol. 14(20), pages 1-20, October.

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