Classification of Dengue Haemorrhagic Fever (DHF) using SVM, naive bayes and random forest

Abstract

Handling Dengue Hemorrhagic Fever (DHF) becomes Indonesia's national priority. DHF is an infectious disease whose treatment requires precision and speed of diagnosis. Data mining can be used to build prediction diagnosing DHF disease with supporting database. This paper aims to predict DHF using SVM, Naive Bayes, and Random Forest and then compare it with the accuracy of the result of third method. The accurate DHF prediction system can be used to avoid the error of diagnosing DHF and the treatment of the disorder can be done more quickly and precisely. The input systems are the patient's medical records (i.e. temperature, spotting, rumple leed, and bleeding) and the output system is suffering from DHF or not.