The Analysis of Determining Cost of Products and Forecasting Dengue Fever Hemorrhagic Incidents: A Machine Learning Approach

DOI

10.2991/aer.k.210810.080How to use a DOI?

Keywords

Forecast, Dengue Hemmorhagic Fever, Machine Learning, Deep Learning, Neural Network, Generalized Linear Model, And KNN

Abstract

Dengue is a viral infection transmitted by Aedes mosquitos. This disease mostly spread in the tropical and sub-tropical countries and according to WHO, the dengue outbreaks has increased 30-fold over the last five decades. The disease is still an ongoing burden of throughout the world. In Indonesia, for example, the incident of dengue hemorrhagic fever (DHF) has shown up 8,056 cases spread in the last five years. One of the ways to help the government to mitigate any possible of the spread is by utilizing a nearly accurate forecast system in predicting the cases. This study aims to employ machine learning methods in predicting the cases occurred in East Kalimantan. Various kinds of data (such as climate, demographical and epidemiological data) are used in developing some machine learning models. Furthermore, identifying variables prior the models’ development is done to achieve the best model of prediction; furthermore, a comparative study of the models built is discussed. Monthly dengue cases, incidence rate (IR), climate factors (rainfall, atmospheric pressure, the duration of the sun) and socio-economic conditions (population density, the number of inhabitants) from three different cities/districts (Samarinda, Balikpapan, and Berau) in East Kalimantan from 2007-2019 are gathered. Prior machine learning’s modeling, all data are analyzed with Pearson Correlation method to identify which variables has a positive correlation with DHF cases. Several machine learning algorithms, those are: Neural Network, Deep Learning, Generalized Linear Model, Generated Boast Tree and KNN, implemented in the modelling and forecasting. The results showed that most climatic factors are negatively correlated to DHF cases in East Kalimatan. Furthermore, the selection of variables leveraged the performance of the models.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tien Rahayu Tulili
AU  - Yohanes K Windi
AU  - Bambang Cahyono
AU  - Damar Nurcahyono
AU  - Karyo Budi Utomo
AU  - Ahmad Rofiq Hakim
PY  - 2021
DA  - 2021/08/11
TI  - The Analysis of Determining Cost of Products and Forecasting Dengue Fever Hemorrhagic Incidents: A Machine Learning Approach
BT  - Proceedings of the 2nd Borobudur International Symposium on Science and Technology (BIS-STE 2020)
PB  - Atlantis Press
SP  - 461
EP  - 466
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.210810.080
DO  - 10.2991/aer.k.210810.080
ID  - Tulili2021
ER  -