Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction

Abdul Mahatir Najar; Mohammad Isa Irawan; Dieky Adzkiya

doi:10.1109/ICSCEE.2018.8538409

Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction

Abdul Mahatir Najar
, Mohammad Isa Irawan
, Dieky Adzkiya

Insitut Teknologi Sepuluh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538648360
DOIs	https://doi.org/10.1109/ICSCEE.2018.8538409
Publication status	Published - 15 Nov 2018
Externally published	Yes
Event	2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 - Shah Alam, Malaysia Duration: 11 Jul 2018 → 12 Jul 2018

Publication series

Name	2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018

Conference

Conference	2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018
Country/Territory	Malaysia
City	Shah Alam
Period	11/07/18 → 12/07/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

DHF
Extreme Learning Machine
Prediction
weather condition

Access to Document

10.1109/ICSCEE.2018.8538409

Cite this

Najar, A. M., Irawan, M. I., & Adzkiya, D. (2018). Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction. In 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 Article 8538409 (2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSCEE.2018.8538409

Najar, Abdul Mahatir ; Irawan, Mohammad Isa ; Adzkiya, Dieky. / Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction. 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018).

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title = "Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction",

abstract = "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.",

keywords = "DHF, Extreme Learning Machine, Prediction, weather condition",

author = "Najar, \{Abdul Mahatir\} and Irawan, \{Mohammad Isa\} and Dieky Adzkiya",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018 ; Conference date: 11-07-2018 Through 12-07-2018",

year = "2018",

month = nov,

day = "15",

doi = "10.1109/ICSCEE.2018.8538409",

language = "English",

series = "2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018",

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Najar, AM, Irawan, MI & Adzkiya, D 2018, Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction. in 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018., 8538409, 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018, Institute of Electrical and Electronics Engineers Inc., 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018, Shah Alam, Malaysia, 11/07/18. https://doi.org/10.1109/ICSCEE.2018.8538409

Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction. / Najar, Abdul Mahatir; Irawan, Mohammad Isa ; Adzkiya, Dieky.
2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8538409 (2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 - https://www.scopus.com/pages/publications/85059417418

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 -

Najar AM, Irawan MI , Adzkiya D. Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction. In 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8538409. (2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018). doi: 10.1109/ICSCEE.2018.8538409

Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Prediction

Abstract

Publication series

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UN SDGs

Keywords

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