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Table 5 Performance of different methods on the real genomic dataset

From: Dynamic model updating (DMU) approach for statistical learning model building with missing data

Technique

Average (MSE (Method)/MSE (Mean Imputation) (S = 5)

NCR (95% CI)

SCR (95% CI)

SLR (95% CI)

–

7.24 (1.03–13.46)

KNN (95% CI)

–

1.00 (0.99–1.01)

SLRM (95% CI)

0.98 (0.97–1.00)

0.98 (0.97–1.00)

RF (95% CI)

1.03 (0.99–1.06)

1.01 (0.99–1.02)

DMU (95% CI)

0.92 (0.86–0.98)

0.97 (0.92–1.02)

  1. SLR Simple Linear Regression, KNN k Nearest Neighbors based Imputation, SLRM Simple Linear Regression combined with imputation, RF Random Forest-based Imputation, DMU Dynamic Model Updating, SCR Some Complete Rows in training data, NCR No Complete Rows in training data, CI Confidence Interval