Machine learning and explainable artificial intelligence for the prevention of waterborne cryptosporidiosis and giardiosis

•Explainable AI as a “toolbox” in early warning systems for waterborne outbreaks.•Random Forest for modelling Cryptosporidium.•Extreme Gradient Boosting and Support Vector Regression for modelling Giardia.•Different combinations of biotic/abiotic markers were informative in each model. Cryptosporidi...

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Veröffentlicht in:	Water research (Oxford) 2024-09, Vol.262, p.122110, Article 122110
Hauptverfasser:	Ligda, Panagiota, Mittas, Nikolaos, Kyzas, George Z., Claerebout, Edwin, Sotiraki, Smaragda
Format:	Artikel
Sprache:	eng
Schlagworte:	algorithms Artificial Intelligence cryptosporidiosis Cryptosporidiosis - epidemiology Cryptosporidiosis - prevention & control Cryptosporidium Cryptosporidium - isolation & purification Cryptosporium Explainable artificial intelligence Giardia Giardia - isolation & purification giardiasis Giardiasis - epidemiology Giardiasis - prevention & control human health Humans Machine Learning Monitoring system Oocysts prediction regression analysis water Waterborne Diseases - prevention & control Waterborne outbreak zoonoses
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