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 language | English |
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Title of host publication | Proceedings of the International Conference on Computing and Communication Technologies, RIVF 2022 |
Editors | Vo Nguyen Quoc Bao, Tran Manh Ha |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 94-99 |
Number of pages | 6 |
ISBN (Electronic) | 9781665461665 |
ISBN (Print) | 9781665461672 |
DOIs | |
Publication status | Published - 18 Jan 2023 |
Event | 16th IEEE-RIVF International Conference on Computing and Communication Technologies - Ho Chi Minh, Viet Nam Duration: 20 Dec 2022 → 22 Dec 2022 |
Publication series
Name | RIVF International Conference on Computing and Communication Technologies: Proceedings |
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Publisher | IEEE |
ISSN (Print) | 2162-786X |
Conference
Conference | 16th IEEE-RIVF International Conference on Computing and Communication Technologies |
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Country/Territory | Viet Nam |
City | Ho Chi Minh |
Period | 20/12/2022 → 22/12/2022 |
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