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Table 1 HS models performance evaluation in external validation datasets

From: Deep learning health space model for ordered responses

 

KNHANES(n = 32,140)

Ewha-Boramae (n = 862)

KARE (n = 3,199)

Average

Health space index

Average

Silhouette score

Calinski-Harabasz index

Davies Bouldin index

Average

Health space index

Average

Silhouette score

Calinski-Harabasz index

Davies Bouldin index

Average

Health space index

Average

Silhouette score

Calinski-Harabasz index

Davies Bouldin index

Deep Ordinal Neural Network

0.74

0.09

49722.82

1.66

0.53

-0.08

487.60

2.41

0.41

0.00

1475.77

3.34

Binary DNN

(0 vs. 3)

0.46

-0.04

4988.67

4.37

0.43

-0.21

121.16

4.16

0.40

-0.15

399.87

4.42

Binary DNN

(0 vs. 1 + 2 + 3)

0.45

-0.08

3386.12

5.83

0.42

-0.28

74.70

9.57

0.36

-0.26

267.53

4.3

Binary DNN

(0 + 1 vs. 2 + 3)

0.57

-0.10

9693.17

10.59

0.51

-0.20

157.85

5.45

0.38

-0.10

759.1

2.68

Binary DNN (0 + 1 + 2 vs. 3)

0.58

-0.18

2681.57

8.17

0.49

-0.29

62.25

13.56

0.29

-0.11

141.49

13.59

Proportional Odds Model

0.43

-0.04

19134.58

5.35

0.47

-0.10

319.78

9.67

0.40

-0.02

882.99

3.19

  1. The performance of HS models is compared using the average HSI, average Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. All the models were trained using the KNHANES dataset and validated on two external datasets, the Ewha-Boramae and KARE datasets. The best-performing HS model for each measure is highlighted in bold