Table 7 Predictors, extracted from PDA data set, ordered according to their significance for each dimensionality reduction method.

From: Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis

Ord

Decision tree

Random forest

Gradient boosting

Logistic regression

LASSO

Expert selection (PDAEXP)

Initial

Num

Avg

Final

1

Size_LD

Size_LD

Koos_LD

Size_LD

Koos_LD

Koos_LD

5

2

Koos_LD

2

Koos_LD

Koos_LD

Size_SC

PTAVSAR4_SC

PTADAR4_IC

Size_LD

4

1.5

-

3

Size_AC

Size_SC

Size_LD

Size_SC

SRT_IC

Size_SC

4

4.5

Size_SC

4

PTAVSAR4_AC

SRT_LD

PTAVSSC4

Koos_LD

SRT_LD

Koos_TD

3

6.7

-

5

Koos_TD

Koos_TD

SRT_SC

PTADAR8_SC

PTAVSAR8_IC

PTADAR4_SC

3

7.7

PTADAR4_SC

6

PTAVSAR8_AC

PTAVSAR8_IC

PTAVSAR8_SC

PTAVSAR8_SC

PTADAR8_LD

PTAVSAR4_SC

3

5.3

PTAVSAR4_SC

7

PTADAR8_IC

PTADAR4_SC

PTAVSAR4_LD

PTAVSAR8_SC

PTADAR8_IC

PTADAR8_IC

3

7.7

8

PTADAR4_SC

PTADAR4_LD

PTADAR8_SC

PTADAR4_SC

PTAVSAR4_IC

Size_AC

3

7.3

9

SRT_AC

PTADAR8_AC

PTADAR8_LD

PTADAR8_IC

Size_AC

10

Size_SC

PTAVSAR4_SC

Size_AC

Koos_TD

PTAVSAR4_AC

  1. Irrelevant variables were rejected from expert selection. Variables and metrics are expressed as follows (see “Methods”): (a) suffix 4 holds for the basic frequency range, suffix 8 holds for the full frequency range; (b) subscripts H and VS hold for the healthy or diseased ear, respectively, subscript D holds for the difference of averaged PTA values between the two ears; (c) tailing abbreviations have the following meaning: AR average row wise, IR intercept row wise, SR slope row wise, AC average column wise, IC intercept column wise, SC slope column wise, LD last difference, TD total difference.