Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay. (29th July 2019)

• Record Type: Journal Article
• Authors: Mello-Román, Jorge D.; Mello-Román, Julio C.; Gómez-Guerrero, Santiago; García-Torres, Miguel
• Other Names: Fantacci Maria E. Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2019(2019)
• Publication Date: 2019-07-29
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2019/7307803
• Languages: English
• ISSNs: 1748-670X

Abstract

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.