Figure 1
Decision tree results for the evaluation of preterm birth based on mothers’ characteristics.
ART, assisted reproductive technology.
Figure 2
Area under the curve analysis of the logistic regression and decision tree methods.
DT, decision trees; LR, logistic regression; AUC, area under the curve.
Table 1Demographic and clinical characteristics of the participants
Variable |
PTB (n = 244) |
Non-PTB (n = 4,171) |
p-value |
Mother’s age (y) |
30.51 ± 5.96 |
29.10 ± 5.31 |
< 0.001 |
SES |
0.17 ± 2.11 |
0.022 ± 2.03 |
0.272 |
Mother’s BMI (kg/m2) |
25.00 ± 4.13 |
24.99 ± 5.61 |
0.970 |
Parity |
1.65 ± 0.78 |
1.65 ± 0.76 |
0.989 |
Mother’s education |
|
|
0.035 |
Non-academic |
149 (61.1) |
2,820 (67.6) |
|
Academic |
95 (38.9) |
1,351 (32.4) |
|
Mother’s occupation |
|
|
0.314 |
Housewife |
209 (85.7) |
3,666 (87.9) |
|
Employed |
35 (14.3) |
505 (12.1) |
|
Type of pregnancy |
|
|
0.617 |
Wanted |
194 (79.5) |
3,369 (80.8) |
|
Unwanted |
50 (20.5) |
802 (19.2) |
|
History of abortion |
|
|
0.243 |
No |
190 (77.9) |
3,373 (80.9) |
|
Yes |
54 (22.1) |
798 (19.1) |
|
History of stillbirth |
|
|
0.199 |
No |
237 (97.1) |
4,101 (98.3) |
|
Yes |
7 (2.9) |
70 (1.7) |
|
Infant sex |
|
|
0.131 |
Male |
136 (55.7) |
2,115 (50.7) |
|
Female |
108 (44.3) |
2,056 (49.3) |
|
Caesarian section |
|
|
0.022 |
No |
52 (21.3) |
1,167 (28.0) |
|
Yes |
192 (78.7) |
3,004 (72.0) |
|
Multiple pregnancy |
|
|
< 0.001 |
No |
210 (86.1) |
4,143 (99.3) |
|
Yes |
34 (13.9) |
28 (0.7) |
|
Preeclampsia |
|
|
< 0.001 |
No |
198 (81.1) |
3,982 (95.5) |
|
Yes |
46 (18.9) |
189 (4.5) |
|
ART |
|
|
< 0.001 |
No |
197 (80.7) |
3,886 (93.2) |
|
Yes |
47 (19.3) |
285 (6.8) |
|
Table 2The results of logistic regression assessing PTB based on mothers’ characteristics
Variable |
AOR (95% CI) |
p-value |
Age |
1.00 (0.96–1.05) |
0.889 |
BMI |
0.99 (0.94–1.04) |
0.648 |
Multiple pregnancy |
28.63 (10.45–78.42) |
< 0.001 |
Preeclampsia |
4.42 (2.12–9.18) |
< 0.001 |
ART |
3.23 (1.69–6.19) |
< 0.001 |
Table 3Accuracy measures of logistic regression and decision tree methods in training and testing subsamples
Model |
Training sample |
Testing sample |
|
|
LR |
DT |
LR |
DT |
Sensitivity |
0.46 |
0.69 |
0.41 |
0.57 |
|
Specificity |
0.83 |
0.59 |
0.88 |
0.59 |
|
Positive predictive value |
0.13 |
0.09 |
0.18 |
0.08 |
|
Negative predictive value |
0.96 |
0.97 |
0.96 |
0.96 |
|
Accuracy |
0.80 |
0.59 |
0.85 |
0.59 |