Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks

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• 作者：Rafaela Beatriz Pintor Torrecilha^a ; Yuri Tani Utsunomiya^b ; Luí ; s F&aacute ; bio da Silva Batista^c ; Anelise Maria Bosco^a ; C&aacute ; ris Maroni Nunes^d ; Paulo Cé ; sar Ciarlini^a ; M&aacute ; rcia Dalastra Laurenti^c ; ^{mdlauren@usp.br}
• 关键词：Canis lupus familiaris ; qPCR ; Machine learning ; Leishmania spp.
• 刊名：Veterinary Parasitology
• 出版年：2017
• 出版时间：30 January 2017
• 年：2017
• 卷：234
• 期：Complete
• 页码：13-18
• 全文大小：563 K
• 卷排序：234

文摘

Leishmania infantum load was predicted from clinical data by a radial basis artificial neural network. Predictions were unbiased and non-inflated, with an accuracy of 0.869. When qPCR is unavailable, parasite load can be estimated using machine learning techniques.