Livesey, Joseph and Wojtczak, Dominik
(2021)
Leveraging Neural Networks in Malaria Control.
In: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2021-10-13 - 2021-10-15.
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Abstract
In this paper we build a neural network model to predict prevalence of malaria for a given geographic location and year. We report on our experience of building the most suitable neural network architecture for this problem. We show that both utilizing dropout and Adam optimizer in the network training process is very effective and can lead to a precise model without overfitting issues. Incorporating rainfall data leads to a significant improvement in the precision of the model, highlighting the fact that this is an important factor in the spread of malaria. We then utilize the selected best neural network to predict the outcome of eradicating malaria at given locations. This can help to decide where to use limited resources, like vaccines or insecticides, for the largest possible impact in malaria control.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | 32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, 46 Information and Computing Sciences, 3207 Medical Microbiology, 4611 Machine Learning, Rare Diseases, Bioengineering, Malaria, Vector-Borne Diseases, Infectious Diseases, Infection, 3 Good Health and Well Being |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 19 Nov 2021 08:13 |
| Last Modified: | 09 Sep 2024 16:05 |
| DOI: | 10.1109/cibcb49929.2021.9562789 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3143444 |
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