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

Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay. Comput Math Methods Med. 2019;2019:7307803 Authors: Mello-Román JD, Mello-Román JC, Gómez-Guerrero S, García-Torres M 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. PMID: 31485259 [PubMed - in process]
Source: Computational and Mathematical Methods in Medicine - Category: Statistics Tags: Comput Math Methods Med Source Type: research