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Table 2 Performance of the four classification methods used to predict the prevalence of B. afzelii, B. garinii, and B. valaisiana in a grid of 0.25° radius over Europe using a combination of environmental data

From: An updated meta-analysis of the distribution and prevalence of Borrelia burgdorferi s.l. in ticks in Europe

 

B. afzelii

B. garinii

B. valaisiana

 

AUC

CA

AUC

CA

AUC

CA

Neural Networks

0.491

0.972

0.585

0.963

0.496

0.989

Naive Bayes

0.550

0.626

0.592

0.662

0.824

0.775

AdaBoost

0.499

0.974

0.500

0.961

0.500

0.989

SVM

0.816

0.987

0.816

0.981

0.844

0.995

  1. AUC area under the curve, CA classification accuracy. The AUC values refer to the average of all the classifications. Complete AUC and CA values for each category can be obtained from the Additional files 2 and 3
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International Journal of Health Geographics

ISSN: 1476-072X