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Table 2 Results of syndrome models for inquiry diagnosis on total labels by using ML-kNN, RankSVM, BPMLL and kNN with different symptom subsets

From: Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

symptoms

Average_Precision(%)

 

ML-kNN

kNN

RankSVM

BPMLL

125

76.2 ± 3.1

74.2 ± 3.3

70.9 ± 3.1

76.1 ± 3.8

106

76.6 ± 2.7

73.7 ± 3.3

71.0 ± 3.4

75.0 ± 3.3

83

76.8 ± 2.4

75.0 ± 3.1

74.3 ± 2.9

75.8 ± 3.4

64

76.6 ± 2.9

75.3 ± 2.9

74.4 ± 2.8

73.9 ± 3.9

52

78.0 ± 2.4

74.7 ± 2.3

73.3 ± 2.6

75.1 ± 2.7

32

75.7 ± 3.2

73.7 ± 3.5

72.1 ± 2.9

75.0 ± 2.7

21

74.9 ± 2.9

73.2 ± 3.8

70.5 ± 3.5

74.4 ± 3.3

symptoms

Coverage

 

ML-kNN

kNN

RankSVM

BPMLL

125

3.28 ± 0.32

3.44 ± 0.23

3.47 ± 0.28

3.30 ± 0.35

106

3.28 ± 0.27

3.41 ± 0.31

3.43 ± 0.28

3.52 ± 0.32

83

3.29 ± 0.28

3.46 ± 0.28

3.38 ± 0.29

3.32 ± 0.38

64

3.22 ± 0.23

3.43 ± 0.23

3.48 ± 0.29

3.41 ± 0.28

52

3.21 ± 0.24

3.43 ± 0.21

3.38 ± 0.35

3.34 ± 0.27

32

3.25 ± 0.31

3.49 ± 0.35

3.41 ± 0.25

3.43 ± 0.23

21

3.26 ± 0.32

3.51 ± 0.35

3.53 ± 0.36

3.42 ± 0.35

symptoms

Ranking_Loss

 

ML-kNN

kNN

RankSVM

BPMLL

125

0.290 ± 0.031

0.394 ± 0.044

0.384 ± 0.032

0.291 ± 0.036

106

0.283 ± 0.029

0.390 ± 0.037

0.351 ± 0.035

0.311 ± 0.031

83

0.277 ± 0.024

0.388 ± 0.037

0.329 ± 0.031

0.337 ± 0.029

64

0.266 ± 0.032

0.384 ± 0.042

0.348 ± 0.040

0.330 ± 0.027

52

0.271 ± 0.028

0.379 ± 0.034

0.353 ± 0.036

0.309 ± 0.048

32

0.273 ± 0.047

0.402 ± 0.036

0.343 ± 0.042

0.294 ± 0.029

21

0.279 ± 0.041

0.414 ± 0.029

0.369 ± 0.044

0.321 ± 0.037