AN INTELLIGENT DENGUE HEMORRHAGIC FEVER SEVERITY LEVEL DETECTION BASED ON DEEP NEURAL NETWORK APPROACH

Keywords: blood, deep learning, dengue, dropout, min-max norm

Abstract

Dengue hemorrhagic fever is one of the most dangerous diseases which often leads to death for the sufferer due to delays or improper handling of the severity that has occurred. In determining that severity level, a specialist analyzes it from the symptoms and blood testing results. This research was developed to produce a system by applying Deep Neural Network approach that is able to give the same analytical ability as a doctor, so that it can give fast and precise decision of dengue handling. The research stages consisted of normalizing data to 0 – 1 intervals by Min-Max method, training data into multilayer networks with fully connected and partially connected schemes to produce the best weights, validating data and final testing. From the use of network parameters as much as 10 input units, 1 bias, 2 hidden layers, 2 output units, learning rate of 0.3, epoch 1000, tolerance rate 0.02, threshold 0.5, the system succeeded in generating a maximum accuracy of 95% in data learning (60 data), 87.5% on data learning and non-learning (40 data), 85% on non-learning data (20 data).

Author Biographies

Dian Pratiwi, Trisakti University
IT lecturer in Trisakti University (Department of Informatics Engineering since 2008 )
Gatot Budi Santoso, Trisakti University
IT lecturer in Trisakti University
Leni Muslimah, Trisakti University
Student of Trisakti University
Raden Davin Rizki, Trisakti University
Student of Trisakti University
Published
2019-07-08