Universidad Nacional de Concepción, Concepción 8700, Paraguay.
Universidad Nacional de Asunción, San Lorenzo 2111, Paraguay.
Comput Math Methods Med. 2019 Jul 29;2019:7307803. doi: 10.1155/2019/7307803. eCollection 2019.
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.
登革热的早期诊断仍然是发病率高的国家公共卫生关注的问题。在这项工作中，我们比较了两种机器学习技术：人工神经网络（ANN）和支持向量机（SVM），作为医学诊断的辅助工具。分类模型的性能在从 2012 年至 2016 年期间从巴拉圭公共卫生系统提取的具有以前登革热诊断的患者的真实数据集上进行了评估。ANN 多层感知器的平均准确率为 96%，灵敏度为 96%，特异性为 97%，数据集的三十个不同分区的变化较小。相比之下，SVM 多项式的准确率、灵敏度和特异性均高于 90%。
