Application of machine learning and CRISP-DM methodology for accurate severity classification of dengue
DOI:
https://doi.org/10.24054/rcta.v1i43.2822Keywords:
Data Science, CRISP-DM, Dengue, Machine LearningAbstract
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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Copyright (c) 2024 Carlos Alberto Mejia Rodriguez, Miguel Alberto Rincon Pinzon, Luis Manuel Palmera Quintero, Lina Marcela Arevalo Vergel
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