Diarrhoea incidence prediction using climate data: machine learning approaches

Thanh Duy Do*, James Mulhall, Thuan Dinh Nguyen, Quang T. M. Nguyen, Diep Phan, Son T. Mai*

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Diarrhoea disease pose significant threats to national morbidity and mortality in Vietnam, especially on children below 5 years old. Being a climate sensitive disease, it has strong links to various meteorological factors like rainfall. Together with global climate changes, these meteorological factors have contributed the increasing of Diarrhoea incidence in Vietnam. Thus, having an effective early warning system is becoming an urgent need. However, it has not been paid enough attention with very few research works, mainly focusing on quantilizing the relationships among climate and diarrhoea incidence using linear regressions. Exploring more sophisticated machine learning techniques is therefore an interesting work towards more efficient and effective warning systems. In this paper, different types of prediction models from traditional to deep learning methods are studied for predicting Diarrhoea rate in six provinces in Vietnam in both long- and short-terms. Experiments show that LSTM-ATT acquires better performance compared to all other approaches like SARIMA, CNN, LSTM, Transformer, and Prophet.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Computing and Communication Technologies, RIVF 2022
EditorsVo Nguyen Quoc Bao, Tran Manh Ha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages94-99
Number of pages6
ISBN (Electronic)9781665461665
ISBN (Print)9781665461672
DOIs
Publication statusPublished - 18 Jan 2023
Event16th IEEE-RIVF International Conference on Computing and Communication Technologies - Ho Chi Minh, Viet Nam
Duration: 20 Dec 202222 Dec 2022

Publication series

NameRIVF International Conference on Computing and Communication Technologies: Proceedings
PublisherIEEE
ISSN (Print)2162-786X

Conference

Conference16th IEEE-RIVF International Conference on Computing and Communication Technologies
Country/TerritoryViet Nam
CityHo Chi Minh
Period20/12/202222/12/2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • attention mechanism-enhanced LSTM (LSTM-ATT)
  • Convolutional Neural Network (CNN)
  • Diarrhoea prediction
  • Long Short-Term Memory (LSTM)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Information Systems and Management

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