Dengue Prediction Using Deep Learning With Long Short-Term Memory
Dengue is a severe infectious disease on the rise in Malaysia, and there is a demand for artificial intelligence to support the health system. However, the application of deep learning, specifically Long Short-Term Memory (LSTM) time series forecasting, has not been explored by many in dengu...
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| Main Authors: | , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
IEEE
2021
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/35862/1/dengue1.pdf http://ir.unimas.my/id/eprint/35862/ https://ieeexplore.ieee.org/document/9515734 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | Dengue is a severe infectious disease on the rise in
Malaysia, and there is a demand for artificial intelligence to
support the health system. However, the application of deep
learning, specifically Long Short-Term Memory (LSTM) time
series forecasting, has not been explored by many in dengue
prediction studies. However, considering the availability of daily
weather data being collected, the ability of LSTM to capture long
term dependencies can be leveraged in forecasting dengue cases.
Therefore, this study investigates the performance and viability of
LSTM time series forecasting on predicting dengue cases. An
LSTM model is developed and evaluated to be compared to a
Support Vector Regression (SVR) model by utilising the
availability of a dengue dataset consisting of weather and climate
data. The results indicated LSTM time series forecasting
performed better than SVR, with R2 and MAE scoring 0.75 and
8.76. In short, LSTM has shown better performance and, in
addition, capturing trends in the rise and fall of dengue cases.
Altogether, this research could contribute to the fight against the
increase of dengue cases without relying on forecasted weather
data but instead, history. |
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