会议论文详细信息
3rd Annual Applied Science and Engineering Conference
Classification of Dengue Haemorrhagic Fever (DHF) using SVM, naive bayes and random forest
工业技术;自然科学
Arafiyah, R.^1 ; Hermin, F.^1 ; Kartika, I.R.^2 ; Alimuddin, A.^3 ; Saraswati, I.^3
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Jakarta State University, Jl. Rawamangun, Muka East Jakarta, Indonesia^1
Department of Chemistry, Faculty of Mathematics and Natural Sciences, Jakarta State University, Jl. Rawamangun, Muka East Jakarta, Indonesia^2
Electrical Engineering Departemen Faculty of Engineering, University of Sultan Ageng Tirtayasa, Jl Jendral Sudirman Km 03, Cilegon, Banten, Indonesia^3
关键词: Dengue haemorrhagic fevers;    Dengue hemorrhagic fever;    Infectious disease;    Input systems;    Medical record;    Naive bayes;    Prediction systems;    Random forests;   
Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/434/1/012070/pdf
DOI  :  10.1088/1757-899X/434/1/012070
来源: IOP
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【 摘 要 】

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.

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