Table 2

Classification rules.

Rule

IDa

Cond 1

Cond 2

Predicted

Outcome

Coveringb

(%)

Errorc

(%)

Fisher

pvalued


1

IF(

217356_s_at ≤ 721

226452_at < 326

)THEN

Good

80

3.5

<0.001

2

IF (

206686_at ≤ 26

226452_at ≤326

)THEN

Good

70

14

<0.001

3

IF (

200738_s_at ≤ 1846

230630_at>23

)THEN

Good

62

10

<0.001

4

IF(

209446_s_at ≤ 57

223172_s_at <73

)THEN

Good

60

10

<0.001

5

IF(

202022_at > 131

223193_x_at < 324

)THEN

Good

60

14

<0.001

6

IF(

224314_s_at ≤ 29

236180_at <13

)THEN

Good

48

7.1

<0.001

7

IF(

217356_s_at > 721

)THEN

Poor

92

17

<0.001

8

IF(

223172_s_at >73

226452_at> 326

)THEN

Poor

60

8.6

<0.001

9

IF(

206686_at > 26

223172_s_at>73

)THEN

Poor

57

7.4

<0.001


a Cond 1 and Cond 2 indicate the conditions into the premises of the rules.

b The covering accounts for the fraction of patients that verify the rule and belong to the target outcome.

c The error accounts for the fraction of patients that satisfy the rule and do not belong to the target outcome.

d Fisher p-value quantifies the statistical significance of the rule on the basis of the number of patients correctly and incorrectly classified by a rule and the number of patients of the dataset belonging to each specific outcome.

Cangelosi et al. BMC Bioinformatics 2014 15(Suppl 5):S4   doi:10.1186/1471-2105-15-S5-S4

Open Data