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Application of machine learning and CRISP-DM methodology for accurate severity classification of dengue

Authors

Carlos Alberto Mejia Rodriguez Universidad Popular del Cesar https://orcid.org/0000-0001-5084-6010
Miguel Alberto Rincon Pinzon Universidad Popular del Cesar https://orcid.org/0000-0002-6827-5209
Luis Manuel Palmera Quintero Universidad Popular del Cesar https://orcid.org/0000-0002-3242-2115
Lina Marcela Arevalo Vergel Universidad popular del cesar https://orcid.org/0000-0002-7731-5444

DOI:

https://doi.org/10.24054/rcta.v1i43.2822

Keywords:

Data Science, CRISP-DM, Dengue, Machine Learning

Abstract

The project focuses on accurately classifying the severity of Dengue cases in Casanare, Colombia, using Machine Learning (ML) and the CRISP-DM methodology. The target variable is "final classification," which categorizes cases into Dengue without warning signs and Dengue with warning signs. Several models and techniques were tested, with 'RandomForest' standing out as the most effective due to its high performance, achieving an accuracy of 100%. This improvement in classification will enable early and precise identification of case severity, which, in turn, can enhance medical care and intervention strategies. The "Dengue Cases in Casanare by hospital service, person type, symptoms, and hospital status" database was used to support the analysis.

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Author Biographies

Carlos Alberto Mejia Rodriguez, Universidad Popular del Cesar

System Engineer, Specialist in Educational Informatics, Master in E-learning. Member of the GIDEATIC Research Group.

Miguel Alberto Rincon Pinzon, Universidad Popular del Cesar

Systems Engineer, Bachelor of Foreign Language: English, Master in Educational Technology Management. Leader of the GIDEATIC Research Group categorized in category C endorsed by the Popular University of Cesar.

Luis Manuel Palmera Quintero, Universidad Popular del Cesar

Systems Engineer, Master's in Information Technology Governance.

Lina Marcela Arevalo Vergel, Universidad popular del cesar

Industrial Engineer, Specialized in Occupational Health, Specialized in Project Management, Specialized in Digital Technologies Applied to Education. Teacher-researcher in the GIDEATIC Research Group.

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Published

2024-03-16 — Updated on 2024-03-16

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How to Cite

[1]

C. A. Mejia Rodriguez, M. A. Rincon Pinzon, L. M. Palmera Quintero, and L. M. Arevalo Vergel, “Application of machine learning and CRISP-DM methodology for accurate severity classification of dengue”, RCTA, vol. 1, no. 43, pp. 78–85, Mar. 2024.