Table 1.
Summary of data.
Fig 1.
Imputed results of a single instance.
Fig 2.
(a) Mean absolute error (MAE) of the imputation when 20% and 30% of the instances contain missing values, and (b) proportion of true values that were contained in the imputation confidence bounds when 20% and 30% of the instances have missing values.
Fig 3.
AUC of different classifiers.
Table 2.
TPR with respect to different FPR.
Table 3.
Performance comparison at operating points corresponding to high sensitivity and specificity.
Table 4.
AUC using different combinations of classifiers and imputation methods.