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Table 6 Top five important risk factors in descending order for different ML algorithms based on the Swiss clinical-based training samples in 10-fold internal statistical cross-validations

From: Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models

ML: random forestML: logistic regressionML: adapt boostingML: linear modelML: K-nearest neighborsML: linear discriminantML: quadratic discriminantML: MCMC GLMM
Breast cancer age onsetAgeBreast cancer age onsetAgeFamily historyAgeBreast cancer age onsetBreast cancer age onset
AgeBreast cancer age onsetAgeBreast cancer age onsetMutationBreast cancer age onsetMutationAge
MutationAshkenazi Jewish originMutationAshkenazi Jewish originAgeMutationAgeMutation
Ashkenazi Jewish originOvarian cancer age onsetAshkenazi Jewish originMutationAshkenazi Jewish originAshkenazi Jewish originAshkenazi Jewish originOvarian cancer age onset
Ovarian cancer age onsetMutationOvarian cancer age onsetOvarian cancer age onsetOvarian cancer age onsetOvarian cancer age onsetOvarian cancer age onsetAshkenazi Jewish origin