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Differential Diagnosis of Dengue and Chikungunya in Colombian Children Using Machine Learning

  • Conference paper
Advances in Artificial Intelligence – IBERAMIA 2018 (IBERAMIA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11238))

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Abstract

Dengue and chikungunya are vector borne diseases endemic in tropical countries around the world, with very similar clinical presentation, which makes it hard for physicians to tell them apart. Here we propose the use of Machine Learning based classifiers to perform differential diagnosis of dengue and chikungunya in pediatric patients, using simple blood test results as predictors instead of symptoms. Three variables (platelet count, white cell count and hematocrit percentage) from 447 pediatric patients from Hospital Infantil Napoleón Franco Pareja were collected to construct a dataset, later partitioned into train and test sets using Stratified Random Sampling. Grid Search with Stratified 5-Fold Cross-Validation was conducted to assess the performance of Logistic Regression, Support Vector Machine, and CART Decision Tree classifiers. Cross-Validation results show a L2 Logistic Regression model with second degree polynomial features outperforming the other models considered, with a cross-validated Receiver Operating Characteristic Area Under the Curve (ROC AUC) score of 0.8694. Subsequent results over the test set showed a 0.8502 ROC AUC score. Despite a reduced sample and a heavily imbalanced data set, ROC AUC score results are promising and support our approach for dengue and chikungunya differential diagnosis.

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Author information

Authors and Affiliations

  1. School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

    William Caicedo-Torres & Jairo Gutiérrez

  2. Department of Computer Science, Universidad Tecnológica de Bolívar, Cartagena, Colombia

    William Caicedo-Torres

  3. Hospital Infantil Napoleón Franco Pareja, Cartagena, Colombia

    Ángel Paternina-Caicedo & Hernando Pinzón-Redondo

  4. Facultad de Medicina, Universidad de Cartagena, Cartagena, Colombia

    Hernando Pinzón-Redondo

Authors
  1. William Caicedo-Torres
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  2. Ángel Paternina-Caicedo
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  3. Hernando Pinzón-Redondo
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  4. Jairo Gutiérrez
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Corresponding author

Correspondence to William Caicedo-Torres .

Editor information

Editors and Affiliations

  1. Universidad Nacional del Sur, Bahía Blanca, Buenos Aires, Argentina

    Guillermo R. Simari

  2. University of Madeira, Funchal, Portugal

    Eduardo Fermé

  3. Universidad Nacional de Piura, Castilla-Piura, Peru

    Flabio Gutiérrez Segura

  4. Universidad Nacional de Trujillo, Trujillo, Peru

    José Antonio Rodríguez Melquiades

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Caicedo-Torres, W., Paternina-Caicedo, Á., Pinzón-Redondo, H., Gutiérrez, J. (2018). Differential Diagnosis of Dengue and Chikungunya in Colombian Children Using Machine Learning. In: Simari, G.R., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_15

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  • DOI: https://doi.org/10.1007/978-3-030-03928-8_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03927-1

  • Online ISBN: 978-3-030-03928-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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