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Table 2 presents the performances of the nine classification methods on the 30-28 dataset.
The five significant SNPs were used to predict risk of thyroid cancer by nine classification methods.
By 10-fold cross-validation, we found that the prediction accuracy of five SNPs was low across all nine classification methods.
Nine classification teams across five developed countries and two developing country settings were included: three in Australia (Brisbane, Sydney, and Perth) and one team each in Norway, Canada, US, South Africa, Malaysia and Sweden.
To examine the prediction ability based on variants with highly significant associations, we use all five SNPs to predict thyroid cancer by nine classification methods (K-nearest neighbors, logistic regression, naïve Bayes, random forest, support vector machine, Bayesian additive regression trees (BART), recursive partitioning, fuzzy rule-based system, boosting).
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