Indonesia tuberculosis morbidity rate forecasting using recurrent neural network

D. Harlianto, S. Mardiyati, D. Lestari, A. H. Zili, S. Devila

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Several insurance companies sell health insurance products that cover tuberculosis risk. One principal component to determine the insurance premium that must be paid by the insured is the morbidity rate. Therefore, morbidity rate forecasting is essential for an insurance company. In this paper, we present the Indonesia tuberculosis morbidity rate forecasting using Recurrent Neural Network (RNN) which is part of deep learning. Min-Max Scaler was applied to the data before it is used as RNN input to achieve better prediction. Unfortunately, the result shows that RNN performance is not satisfactory due to limited morbidity rate data in Indonesia.

Original languageEnglish
Title of host publicationProceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
EditorsTerry Mart, Djoko Triyono, Tribidasari Anggraningrum Ivandini
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735420014
DOIs
Publication statusPublished - 1 Jun 2020
Event5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 - Depok, Indonesia
Duration: 9 Jul 201910 Jul 2019

Publication series

NameAIP Conference Proceedings
Volume2242
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
CountryIndonesia
CityDepok
Period9/07/1910/07/19

Keywords

  • deep learning
  • morbidity rate
  • RNN
  • Tuberculosis

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  • Cite this

    Harlianto, D., Mardiyati, S., Lestari, D., Zili, A. H., & Devila, S. (2020). Indonesia tuberculosis morbidity rate forecasting using recurrent neural network. In T. Mart, D. Triyono, & T. A. Ivandini (Eds.), Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 [030006] (AIP Conference Proceedings; Vol. 2242). American Institute of Physics Inc.. https://doi.org/10.1063/5.0010445