Dengue classification method using support vector machines and cross-validation techniques

Abstract

Dengue is a dangerous disease that can lead to death if the diagnosis and treatment are not appropriate. The common symptoms that occur, including headache, muscle aches, fever, and rash. Dengue is a disease that causes an endemic in several countries in South Asia and Southeast Asia. There are three varieties of dengue, such as dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). These diseases can currently be classified using a machine learning approach with the input data is the dengue symptoms. This study aims to classify dengue types which consist of three classes: DF, DHF, and DSS using five classification methods including C.45, decision tree, k-nearest neighbor (KNN), random forest, and support vector machine (SVM). The dataset used consists of 21 attributes which are the dengue symptoms. It was collected from 110 patients. The evaluation method was carried out using cross-validation with k-fold 3, 5, and 10. The dengue classification method was evaluated using three parameters: precision, recall, and accuracy were most optimally achieved. The most optimal evaluation results were obtained using SVM with k-fold 3 and 10 with sensitivity, specificity, and accuracy values reaching 99.10%, 99.10%, and 99.09%, respectively.