Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay.

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ID: 37468
2019
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Abstract
Early diagnosis of dengue continues to be a concern for public health in countries with a high incidence of this disease. In this work, we compared two machine learning techniques: artificial neural networks (ANN) and support vector machines (SVM) as assistance tools for medical diagnosis. The performance of classification models was evaluated in a real dataset of patients with a previous diagnosis of dengue extracted from the public health system of Paraguay during the period 2012-2016. The ANN multilayer perceptron achieved better results with an average of 96% accuracy, 96% sensitivity, and 97% specificity, with low variation in thirty different partitions of the dataset. In comparison, SVM polynomial obtained results above 90% for accuracy, sensitivity, and specificity.
Reference Key
melloromn2019predictivecomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mello-Román, Jorge D;Mello-Román, Julio C;Gómez-Guerrero, Santiago;García-Torres, Miguel;
Journal computational and mathematical methods in medicine
Year 2019
DOI
10.1155/2019/7307803
URL
Keywords Keywords not found

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