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A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England

1
Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran
2
Fashion Business School, London College of Fashion, University of the Arts London, London WC1V 7EY, UK
3
ISEM UMR226, Université de Montpellier, CNRS, IRD, EPHE, 34090 Montpellier, France
4
Department of Environmental and Life Science, Faculty of Science and Technology, Bournemouth University, Talbot Campus, Poole BH12 5BB, UK
5
Department of Accounting, Islamic Azad University, Central Tehran Branch, Tehran 1955847781, Iran
*
Author to whom correspondence should be addressed.
Stats 2019, 2(1), 89-103; https://doi.org/10.3390/stats2010007
Received: 15 July 2018 / Revised: 8 January 2019 / Accepted: 23 January 2019 / Published: 5 February 2019
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Abstract

Disease emergence, in the last decades, has had increasingly disproportionate impacts on aquatic freshwater biodiversity. Here, we developed a new model based on Support Vector Machines (SVM) for predicting the risk of freshwater fish disease emergence in England. Following a rigorous training process and simulations, the proposed SVM model was validated and reported high accuracy rates for predicting the risk of freshwater fish disease emergence in England. Our findings suggest that the disease monitoring strategy employed in England could be successful at preventing disease emergence in certain parts of England, as areas in which there were high fish introductions were not correlated with high disease emergence (which was to be expected from the literature). We further tested our model’s predictions with actual disease emergence data using Chi-Square tests and test of Mutual Information. The results identified areas that require further attention and resource allocation to curb future freshwater disease emergence successfully. View Full-Text
Keywords: biodiversity; conservation; management; policies; non native introduction; forecasting; support vector machines biodiversity; conservation; management; policies; non native introduction; forecasting; support vector machines
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MDPI and ACS Style

Hassani, H.; Silva, E.S.; Combe, M.; Andreou, D.; Ghodsi, M.; Yeganegi, M.R.; Gozlan, R.E. A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England. Stats 2019, 2, 89-103.

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