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dc.contributor.editorEscalante H.J.
dc.contributor.editorMontes-y-Gomez M.
dc.contributor.editorSegura A.
dc.contributor.editorde Dios Murillo J.
dc.creatorCaicedo-Torres W.
dc.creatorPaternina Á.
dc.creatorPinzón H.
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 247-258
dc.description.abstractInfection by dengue-virus is prevalent and a public health issue in tropical countries worldwide. Also, in developing nations, child populations remain at risk of adverse events following an infection by dengue virus, as the necessary care is not always accessible, or health professionals are without means to cheaply and reliably predict how likely is for a patient to experience severe Dengue. Here, we propose a classification model based on Machine Learning techniques, which predicts whether or not a pediatric patient will be admitted into the pediatric Intensive Care Unit, as a proxy for Dengue severity. Different Machine Learning techniques were trained and validated using Stratified 5-Fold Cross-Validation, and the best model was evaluated on a disjoint test set. Cross-Validation results showed an SVM with Gaussian Kernel outperformed the other models considered, with an 0.81 Receiver Operating Characteristic Area Under the Curve (ROC AUC) score. Subsequent results over the test set showed a 0.75 ROC AUC score. Validation and test results are promising and support further research and development. © Springer International Publishing AG 2016.
dc.format.mediumRecurso electrónico
dc.publisherSpringer Verlag
dc.titleMachine learning models for early dengue severity prediction
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dc.source.event15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016
dc.subject.keywordsLogistic regression
dc.subject.keywordsMachine learning
dc.subject.keywordsNaive bayes
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsHealth risks
dc.subject.keywordsIntensive care units
dc.subject.keywordsLearning algorithms
dc.subject.keywordsLogistic regressions
dc.subject.keywordsNaive bayes
dc.subject.keywordsLearning systems
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.relation.conferencedate23 November 2016 through 25 November 2016

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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.