Machine learning models for early dengue severity prediction

Infection 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.

• Fecha de publicación: 2016
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/8998
• Acceso en línea: https://hdl.handle.net/20.500.12585/8998
• Palabra clave: Children; Dengue; Logistic regression; Machine learning; Naive bayes; PICU; Severity; SVM; Artificial intelligence; Health risks; Intensive care units; Learning algorithms; Pediatrics; Viruses; Children; Dengue; Logistic regressions; Naive bayes; PICU; Severity; Learning systems
• Rights: restrictedAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/