Traditional Machine Learning based on Atmospheric Conditions for Prediction of Dengue Presence

Authors

  • Brenda Sofía Sánchez López Universidad de Lima
  • Daniela Candioti Nolberto Universidad de Lima
  • José Antonio Taquía Gutiérrez Universidad de Lima Research Institute
  • Yvan García López Universidad de Lima

DOI:

https://doi.org/10.13053/cys-27-3-4383

Keywords:

Dengue outbreak, machine learning, SVM, classification, meteorology

Abstract

The dengue virus has become an increasingly critical problem for humanity due to its extensive spread. This is transmitted through a vector that sprouts in certain climatic conditions (tropical and subtropical climates). The transmission of the disease can be associated with certain climatic variables that reinforce the outbreak. Data were collected on dengue cases by epidemiological week registered in Loreto-Peru from January 1, 2016, to January 31, 2022. Likewise, data on meteorological variables (maximum and minimum temperature; dry and humid bulb temperature; wind speed and total precipitation in the area). In this study, four Machine learning modeling techniques were considered: Support Vector Machine (SVM), Decision Tree, Random Forest and AdaBoost; and the parameters defined to evaluate the models are: Accuracy, Precision, Recall and F-1. As a result, optimal AUC values were obtained in a range from 0.818 to 0.996 for the SVM, Random Forest and AdaBoost algorithms, likewise, in all weather stations the ROC curve showed good performance for all models, except for the Decision Tree algorithm. As a conclusion for this study the optimal model to associate dengue cases with climatic conditions is SVM.

Author Biographies

Brenda Sofía Sánchez López, Universidad de Lima

Member of Industry 4.0 Research GroupResearcher at Carrera de Ingeniería IndustrialIndustrial Engineer bachelor

Daniela Candioti Nolberto, Universidad de Lima

Member of Industry 4.0 Research GroupResearcher at Carrera de Ingeniería IndustrialIndustrial Engineer bachelor

José Antonio Taquía Gutiérrez, Universidad de Lima Research Institute

José Antonio Taquía is a Doctoral Researcher from Universidad Nacional Mayor de San Marcos and holds a Master of Science degree in Industrial Engineering from University of Lima. He is a member of the School of Engineering and Architecture teaching courses on quantitative methods, predictive analytics, and research methodology. He has a vast experience on applied technology related to machine learning and industry 4.0 disrupting applications. In the private sector he was part of several implementations of technical projects including roles as an expert user and in the leading deployment side. He worked as a senior corporate demand planner with emphasis on the statistical field for a multinational Peruvian company in the beauty and personal care industry with operations in Europe and Latin America. Mr. Taquía has a strong background in supply chain analytics and operations modeling applied at different sectors of the industry. He is also a member of the Scientific Research Institute at the Universidad de Lima being part of the exponential technology and circular economy groups. His main research interests are on statistical learning, predictive analytics, and industry 4.0.  

Yvan García López, Universidad de Lima

MBA por la Maastricht School of Management (Países Bajos), magíster en Administración Estratégica de Empresas por la Pontificia Universidad Católica del Perú, y magíster en Ciencias de la Computación por la Aerospace Technical Center del Technological Institute of Aeronautic (Brasil). Cuenta con estudios en ciencias de datos e inteligencia artificial en Northwestern University, University of California Berkeley, University of Pennsylvania y el MIT. Es investigador, data scientist y docente de pregrado y posgrado. Tiene más 20 años de experiencia en la conducción de proyectos de inversión, ejecución, y puesta en operación del desarrollo de arquitectura empresarial tecnológica, infraestructura de TI, telecomunicaciones, telefonía VoIP, y en implantaciones de ERP World Class como PeopleSoft, Oracle E-Business Suite y SAP. Ha sido vicedecano de Ingeniería de Información en Universidad del Pacífico, y director de la Maestría de Tecnología de Información de Centrum Católica. Actualmente, es docente de la Carrera de Ingeniería Industrial de la Universidad de Lima.

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Published

2023-09-26

Issue

Vol. 27 No. 3 (2023): 27(3) 2023

Section

Articles

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