Binary Models for Arboviruses Classification Using Machine Learning: A Benchmarking Evaluation

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dc.contributor.authorDa Silva Neto, Sebastião Rogerio
dc.contributor.authorTabosa, Thomás
dc.contributor.authorMedeiros Neto, Leonides
dc.contributor.authorTeixeira, Igor Vitor
dc.contributor.authorSadok, Sara
dc.contributor.authorDe Souza Sampaio, Vanderson
dc.contributor.authorEndo, Patricia Takako
dc.date.accessioned2022-12-27T19:05:46Z
dc.date.available2022-12-27T19:05:46Z
dc.date.issued2023-01-03
dc.description.abstractArboviral diseases are common worldwide. Infection with arboviruses can lead to serious health problems, even death in severe cases. Such health problems can be prevented by the early and correct detection of these arboviruses, but this is challenging due to the overlap of their symptoms. In this work, we benchmark different Machine Learning (ML) models to classify two types of arboviruses. We propose two distinct binary models: (i) a model to classify if the patient has arbovirus or another disease; and (ii) a model to classify if the patient has Dengue or Chikungunya. We configure and evaluate several ML models using hyperparameter optimization and feature selection techniques. The Random Forest and XGboost tree-based models present the best results with over 80% recall in the Chikungunya and Inconclusive classes.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.348
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/102979
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Healthcare Processes and Services
dc.subjectarbovirus
dc.subjectartificial intelligence
dc.subjectclassification
dc.subjectmachine learning
dc.titleBinary Models for Arboviruses Classification Using Machine Learning: A Benchmarking Evaluation
dc.type.dcmitext
prism.startingpage2820

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