International Journal on Advanced Science, Engineering and Information Technology, Vol. 8 (2018) No. 5, DOI:10.18517/ijaseit.8.5.5006

Early Detection of Dengue Disease Using Extreme Learning Machine

Suhaeri Suhaeri, Nazri Mohd Nawi, Muhamad Fathurahman


Dengue Disease is on of the serious and dangerous disease that cause many mortality and spread in most area in Indonesia. There are about 201,885 cases had been reported in 2016 including 1,585 death cases. The availability of nowadays clinical data of dengue disease can be used to train machine learning algorithm in order to automaticaly detect the present of dengue disease of the patients.  This study will use the Extreme Learning Machine (ELM) to develop a method to classify the dengue by using the clinical data so that first aid can be given in expectation of decreasing death risk. The proposed ELM is an improved model of Neural Network that solves some drawbacks of Backpropagation algorithm that uses during the training phase of Neural Network. The result shows that the proposed ELM with selected clinical features can produce best generalization performance and can predict accurately with 96.94% accuracy.


Machine learning, artificial neural networks, back propagation algorithm, dengue disease, extreme learning machine

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