Data mining for dengue hemorrhagic fever (DHF) prediction with naive Bayes method

Abstract

Handling of infectious diseases is determined by the accuracy and speed of diagnosis. Government through the Regulation of the Minister of Health of the Republic of Indonesia No. 82 of 2014 on the Control of Communicable Diseases establishes Dengue Hemorrhagic Fever (DHF) has made DHF prevention a national priority. Various attempts were made to overcome this misdiagnosis. The treatment and diagnosis of DHF using ANFIS has result an application program that can decide whether a patient has dengue fever or not [1]. An expert system of dengue prevention by using ANFIS has predict the weather and the number of sufferers [2]. The large number of data on DHF often cannot affect a person in making decisions. The use of data mining method, able to build data base support in decision makers diagnose DHF disease [3]. This study predicts DHF with the method of Naive Bayes. Parameter of The input variable is the patient's medical data (temperature, spotting, bleeding, and tornuine test) and the output variable suffers from DBD or not while the system output is diagnosis of the patient suffering from DHF or not. Result of model test by using tools of Orange 3.4.5 obtained level of precision model is 77,3%.