Table 1 Baseline characteristics of study population.

From: Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data

Variables

 

Overall

No

Yes

P-value

N

 

10,000

8707

1293

 

Male gender, n (%)

No

5817 (58.2)

4694 (53.9)

1123 (86.9)

 < 0.001

Yes

4183 (41.8)

4013 (46.1)

170 (13.1)

Age, median [Q1,Q3]

 

76.0 [71.0,82.0]

76.0 [71.0,81.0]

79.0 [74.0,85.0]

 < 0.001

Hypertension, n (%)

No

3288 (32.9)

3010 (34.6)

278 (21.5)

 < 0.001

Yes

6712 (67.1)

5697 (65.4)

1015 (78.5)

CHD, n (%)

No

7426 (74.3)

6570 (75.5)

856 (66.2)

 < 0.001

Yes

2574 (25.7)

2137 (24.5)

437 (33.8)

Lipid disorder, n (%)

No

5880 (58.8)

5247 (60.3)

633 (49.0)

 < 0.001

Yes

4120 (41.2)

3460 (39.7)

660 (51.0)

Stroke, n (%)

No

9373 (93.7)

8187 (94.0)

1186 (91.7)

0.002

Yes

627 (6.3)

520 (6.0)

107 (8.3)

Heart failure, n (%)

No

8431 (84.3)

7464 (85.7)

967 (74.8)

 < 0.001

Yes

1569 (15.7)

1243 (14.3)

326 (25.2)

Cancer, n (%)

No

8287 (82.9)

7282 (83.6)

1005 (77.7)

 < 0.001

Yes

1713 (17.1)

1425 (16.4)

288 (22.3)

Diabetes, n (%)

No

6856 (68.6)

5987 (68.8)

869 (67.2)

0.276

Yes

3144 (31.4)

2720 (31.2)

424 (32.8)

COPD, n (%)

No

8711 (87.1)

7684 (88.3)

1027 (79.4)

 < 0.001

Yes

1289 (12.9)

1023 (11.7)

266 (20.6)

Chronic kidney disease, n (%)

No

8699 (87.0)

7629 (87.6)

1070 (82.8)

 < 0.001

Yes

1301 (13.0)

1078 (12.4)

223 (17.2)

  1. First quartile (Q1), third quartile (Q3).