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. 2020 Mar 10;20:52. doi: 10.1186/s12911-020-1060-0

Table 1.

Accuracy of proposed model and predictors trained with full ClinVar version 2017-05-30, according to ClinVar version 2019-09-23

Classifier Accuracy
*Mean
(± Std. Dev.)
Predictor = N,
Clinvar = 0
*Mean
(± Std. Dev.)
Predictor = P,
Clinvar = 0
*Mean
(± Std. Dev.)
Predictor = N,
Clinvar = 1
*Mean
(± Std. Dev.)
Predictor = P,
Clinvar = 1
*Mean
(± Std. Dev.)
Extreme Gradient Boosting 93 (0.3) 92 (0.5) 8 (0.5) 7 (0.3) 93 (0.3)
* Proposed Tree 92 (0.3) 91 (0.5) 9 (0.5) 8 (0.3) 92 (0.3)
Random Forest 92 (0.3) 91 (0.5) 9 (0.5) 8 (0.3) 92 (0.3)
Bagging 92 (0.3) 90 (0.5) 10 (0.5) 8 (0.3) 92 (0.3)
K Nearest Neighbors 92 (0.3) 89 (0.5) 11 (0.5) 6 (0.3) 94 (0.3)
Ada Boost 92 (0.3) 93 (0.5) 7 (0.5) 8 (0.3) 92 (0.3)
Extra Trees 91 (0.3) 90 (0.5) 10 (0.5) 8 (0.3) 92 (0.3)
Extra Tree 91 (0.3) 90 (0.5) 10 (0.5) 8 (0.3) 92 (0.3)
Linear Discriminant Analysis 91 (0.3) 88 (0.6) 12 (0.6) 8 (0.3) 92 (0.3)
Support Vector Machines (Linear kernel) 91 (0.3) 86 (0.6) 14 (0.6) 6 (0.3) 94 (0.3)
SKLearn Decision Tree 91 (0.3) 90 (0.5) 10 (0.5) 8 (0.3) 92 (0.3)
Multilayer Perceptron 91 (0.3) 85 (0.6) 15 (0.6) 6 (0.3) 94 (0.3)
Quadratic Discriminant Analysis 91 (0.3) 88 (0.5) 12 (0.5) 8 (0.3) 92 (0.3)
Bernoulli Naive Bayes 91 (0.3) 86 (0.6) 14 (0.6) 7 (0.3) 93 (0.3)
Support Vector Machines (RBF Kernel) 91 (0.3) 86 (0.6) 14 (0.6) 7 (0.3) 93 (0.3)
Logistic Regression 91 (0.3) 86 (0.6) 14 (0.6) 7 (0.3) 93 (0.3)
Gaussian Naive Bayes 90 (0.3) 84 (0.6) 16 (0.6) 6 (0.3) 94 (0.3)
Nu-Support Vector Machines 87 (0.4) 82 (0.6) 18 (0.6) 11 (0.3) 89 (0.3)
PROVEAN 83 (0.4) 75 (0.7) 25 (0.7) 13 (0.4) 87 (0.4)
MetaSVM 81 (0.4) 69 (0.6) 31 (0.6) 10 (0.4) 90 (0.4)
Polyphen 80 (0.4) 82 (0.8) 18 (0.8) 20 (0.3) 80 (0.3)
SIFT 80 (0.4) 77 (0.8) 23 (0.8) 18 (0.4) 82 (0.4)

*Mean and standard were calculated from 1000 random samples, each one with 30% of ClinVar version 2019-09-23