TY - GEN
T1 - Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction
AU - Najar, Abdul Mahatir
AU - Irawan, Mohammad Isa
AU - Adzkiya, Dieky
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/15
Y1 - 2018/11/15
N2 - Dengue Hemorrhagic Fever (DHF) is one of the major health problems in Indonesia. With increasing mobility and population density, weather changes, other epidemic factors, the number of dengue fever patients also increases. In order to optimize the prevention of DHF outbreaks, it is important to obtain predictions related to the risk level of DHF outbreak, because each region needs to be treated according to its risk level. The spread of DHF is closely related to weather conditions. Therefore in this study, we apply extreme learning machine (ELM) method to predict the risk of outbreak based on weather condition. We Develop ELM architecture with weather variables as input nodes and risk level of DHF outbreak as the target. We use binary sigmoid activation function and bipolar sigmoid with a number of hidden neurons between 5 200 nodes. The results show that ELM can predict the level of risk of DHF with the best performance of ELM network using a binary sigmoid activation function with 50 hidden neurons.
AB - Dengue Hemorrhagic Fever (DHF) is one of the major health problems in Indonesia. With increasing mobility and population density, weather changes, other epidemic factors, the number of dengue fever patients also increases. In order to optimize the prevention of DHF outbreaks, it is important to obtain predictions related to the risk level of DHF outbreak, because each region needs to be treated according to its risk level. The spread of DHF is closely related to weather conditions. Therefore in this study, we apply extreme learning machine (ELM) method to predict the risk of outbreak based on weather condition. We Develop ELM architecture with weather variables as input nodes and risk level of DHF outbreak as the target. We use binary sigmoid activation function and bipolar sigmoid with a number of hidden neurons between 5 200 nodes. The results show that ELM can predict the level of risk of DHF with the best performance of ELM network using a binary sigmoid activation function with 50 hidden neurons.
KW - DHF
KW - Extreme Learning Machine
KW - Prediction
KW - weather condition
UR - http://www.scopus.com/inward/record.url?scp=85059417418&partnerID=8YFLogxK
U2 - 10.1109/ICSCEE.2018.8538409
DO - 10.1109/ICSCEE.2018.8538409
M3 - Conference contribution
AN - SCOPUS:85059417418
T3 - 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018
BT - 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018
Y2 - 11 July 2018 through 12 July 2018
ER -
