A total of 3882 infants were included into the study according to the inclusion/exclusion criteria, out of them 2011(51.8%) had received delivery room CPR. The efforts were taken for the CPR, in the order of frequency, were: nasal CPAP (n = 1120, P = 28.8%), PPV (n = 891, P = 22.9%), Oxygen mask (n = 723, P = 18.6%), intubation (n = 494, P = 12.7%), chest compression (n = 86, P = 2.2%) and epinephrine injection (n = 68, P = 1.7%). Data statistics are shown in Tables 2 and 3.
Table 2
Descriptive statistics of discrete risk factors
|
Variable name
|
Values
|
Frequency
|
Percent
|
P-Value
|
Gestational risk factors
|
|
Prenatal care
|
Yes
No
|
3426
456
|
88.25
11.75
|
.000
|
|
Chorioamnionitis
|
Yes
No
|
71
3811
|
1.83
98.17
|
0.958
|
|
Steroid administration
|
Yes
No
|
933
2949
|
24.03
75.97
|
.000
|
|
Magnesium sulfate administration
|
Yes
No
|
333
3549
|
8.58
91.42
|
.000
|
Maternal risk factors
|
|
Hypertension
|
Yes
No
|
184
3698
|
4.74
95.26
|
0.00752
|
|
Gestational hypertension (Ghypertension)
|
Yes
No
|
654
3228
|
16.85
83.15
|
0.0005595
|
|
Diabetes
|
Yes
No
|
105
3777
|
2.71
97.29
|
0.7826
|
|
Gestational diabetes (Gdiabetes)
|
Yes
No
|
600
3282
|
15.46
84.54
|
0.5829
|
|
Mother addiction
|
Yes
No
|
63
3819
|
1.62
98.38
|
0.006855
|
|
Mother HIV
|
Yes
No
|
28
3854
|
0.72
99.28
|
0.848
|
|
Cardiac diseases
|
Yes
No
|
304
3578
|
7.83
92.17
|
0.06417
|
|
Blood diseases
|
Yes
No
|
187
3695
|
4.82
95.18
|
0.8656
|
|
Kidney diseases
|
Yes
No
|
63
3819
|
1.62
98.38
|
0.9263
|
|
Thyroid Disorders
|
Hyperthyroidism
Hypothyroidism
Thyroidectomy
No
|
15
694
2
3171
|
0.39
17.88
0.05
81.68
|
0.2737
|
|
Respiratory diseases
|
Yes
No
|
28
3854
|
0.72
99.28
|
0.8509
|
|
Mental diseases
|
Yes
No
|
21
3861
|
0.54
99.46
|
0.9576
|
|
Infectious diseases
|
Yes
No
|
16
3866
|
0.41
99.59
|
0.885
|
|
Brain diseases
|
Yes
No
|
62
3820
|
1.6
98.4
|
0.211
|
|
Cancer diseases
|
Yes
No
|
33
3849
|
0.85
99.15
|
0.5051
|
|
Skin diseases
|
Yes
No
|
7
3875
|
0.18
99.82
|
0.6354
|
|
Liver diseases
|
Yes
No
|
63
3819
|
1.62
98.38
|
0.8716
|
|
Autoimmune diseases
|
Yes
No
|
64
3818
|
1.65
98.35
|
0.771
|
|
Uterus diseases
|
Yes
No
|
41
3841
|
1.06
98.94
|
0.5801
|
|
Digestive diseases
|
Yes
No
|
34
3848
|
0.88
99.12
|
0.3677
|
|
Eye diseases
|
Yes
No
|
4
3878
|
0.10
99.9
|
0.9424
|
|
Other chronic disease ( Mother)
|
Yes
No
|
12
3870
|
0.31
99.69
|
0.1073
|
|
(Pre)Eclampsia
|
Eclampsia
Preeclampsia
No
|
8
198
3676
|
0.21
5.10
94.69
|
0.192
|
|
Abortion history
|
Yes
No
|
17
3865
|
0.44
99.56
|
0.925
|
|
Intrauterine Fetal Death (IUFD)
|
Yes
No
|
10
3872
|
0.26
99.74
|
0.1671
|
Infertility
|
|
Female infertility
|
Yes
No
|
214
3668
|
5.51
94.49
|
.000
|
|
Assisted reproductive techniques (ART)
|
Yes
No
|
144
3738
|
3.71
96.29
|
0.0009746
|
|
Name of ART technique
|
No
Drug administration
IUI
IVF
|
3738
26
18
100
|
96.29
0.67
0.46
2.58
|
0.000333
|
Accreta status
|
|
Decollement / Placenta abruption
|
Yes
No
|
41
3841
|
1.06
98.94
|
0.03364
|
|
Vasa Previa
|
Yes
No
|
1
3881
|
0.03
99.97
|
0.3347
|
|
Previa
|
Yes
No
|
113
3769
|
2.91
97.09
|
0.5083
|
|
Placenta Accreta
|
Yes
No
|
163
3719
|
4.2
95.8
|
0.1257
|
Fetal Data
|
|
Number of infants
|
1
2
3
4
|
3407
419
55
1
|
87.76
10.79
1.42
0.03
|
.000
|
|
Sex
|
Female
Male
Ambiguous Genitalia
|
1730
2146
6
|
44.57
55.28
0.15
|
0.3964
|
|
Rank of infant
|
1
2
3
|
3628
235
19
|
93.46
6.05
0.49
|
.000
|
|
IUGR
|
Yes
No
|
223
3659
|
5.75
94.25
|
0.01075
|
|
Tumors
|
Yes
No
|
14
3868
|
0.36
99.64
|
0.6887
|
|
Genetic problems/ Anomaly
|
Yes
No
|
18
3864
|
0.46
99.54
|
0.08225
|
|
Macrosomia
|
Yes
No
|
19
3863
|
0.49
99.51
|
0.001636
|
|
Cardiac problems
|
Yes
No
|
31
3851
|
0.8
99.2
|
0.983
|
|
Surgery (Defect of the abdominal)
|
Yes (including Colonic atresia, diaphragmatic hernia, duodenal atresia, Esophageal atresia, Gastroschisis,
internal hernia, Intestinal atresia,
Jejunal atresia,
Omphalocele)
No
|
54
3828
|
1.39
98.61
|
0.2758
|
|
Blood problems
|
Yes
No
|
4
3878
|
0.10
99.9
|
0.9424
|
|
Pulmonary problems
|
Yes
No
|
12
3870
|
0.31
99.69
|
0.1073
|
|
Brain problem
|
Yes
No
|
25
3857
|
0.64
99.36
|
0.9842
|
|
Fetal hydrops
|
Yes
No
|
12
3870
|
0.31
99.69
|
0.02858
|
|
Other Problems(Fetus)
|
Yes
No
|
6
3876
|
0.15
99.85
|
0.9295
|
Delivery risk factors
|
|
Delivery type
|
Cesarean
Vaginal
|
3617
265
|
93.17
6.83
|
.000
|
|
PROM
|
Yes
No
|
549
3333
|
14.14
85.86
|
0.0708
|
|
Presentation
|
Breech
Transverse
Hand
Normal
|
106
6
1
3769
|
2.73
0.15
0.03
97.09
|
0.07257
|
|
Cord
|
Absent Doppler
Cord prolapse
Reverse
No
|
27
4
1
3850
|
0.69
0.10
0.03
99.18
|
0.2395
|
|
Thick meconium
|
Yes
No
|
24
3858
|
0.62
99.38
|
0.1438
|
|
Amniotic fluid
|
Oligohydramnios
Polyhydramnios
Normal
|
43
26
3813
|
1.11
0.67
98.22
|
0.04057
|
|
Fetal Heart Rate (FHR)
|
Arrhythmia
BPP
Bradycardia
Tachycardia
Decreased FHR
Fetal distress
Sinusoidal
PVC
No
|
1
2
6
10
269
8
1
1
3584
|
0.03
0.05
0.15
0.26
6.93
0.21
0.03
0.03
92.31
|
0.3952
|
Table 3
Descriptive statistics of continuous risk factors
Variable name
|
Mean
|
SD
|
P-Value [95% confidence interval]
|
Maternal age (Year)
|
30.89
|
5.9
|
0.4742 [-0.1497735, 0.3219842]
|
Gestational age(day)
|
247.15
|
25.17
|
1.179099e-158 [19.21917, 22.09842]
|
PROM hrs.
|
7.84
|
62.41
|
0.0004639 [-10.95175, -3.09293]
|
In order to develop the prediction model for the need to in-birth CPR, ML algorithms were used. Figure 1 displays the results obtained from implementing these algorithms on the original data set.
Figure 1 shows that based on all performance criteria the SVM method has had the best performance in predicting the need to in-birth CPR. Also, J48 method reached comparable results. In the next step of simulation, FS algorithms were employed. For this purpose, seven filter FS algorithms including Relief, Correlation-based feature selection (CFS), Pearson's Correlation, gain ratio, info gain, OneR and symmetrical uncertainty as well as three wrapper methods using three classifiers SVM, J48 and NB were implemented (appendix A). Then, for each risk factor, the total importance resulting from implementing 10 FS algorithms was calculated. Table 4 presents the rank resulting from implementing the FS algorithms as well as the averaged rank for each variable. The averaged rank was calculated using the following relation, where ri represents the rank of variable in the ith feature selection algorithm.
Table 4
Rank of attributes based on 10 feature selection methods
#
|
Variable names
|
Relief
|
CFS
|
Correlation
|
Gain ratio
|
Info gain
|
OneR
|
symmetrical uncertainty
|
Wrapper (SVM)
|
Wrapper (NB)
|
Wrapper (J48)
|
Averaged rank
|
1
|
GA
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
2
|
Delivery type
|
5
|
4
|
5
|
8
|
6
|
4
|
5
|
4
|
2
|
2
|
4.5
|
3
|
Presentation
|
10
|
7
|
17
|
21
|
15
|
6
|
14
|
6
|
17
|
8
|
12.1
|
4
|
Steroids administration
|
2
|
2
|
2
|
3
|
2
|
2
|
2
|
2
|
46
|
60
|
12.3
|
5
|
Macrosomia
|
22
|
18
|
13
|
2
|
13
|
9
|
11
|
9
|
3
|
24
|
12.4
|
6
|
Prenatal care
|
6
|
6
|
6
|
14
|
5
|
3
|
6
|
3
|
47
|
39
|
13.5
|
7
|
Infant number
|
8
|
3
|
3
|
5
|
3
|
5
|
3
|
5
|
58
|
45
|
13.8
|
8
|
Addiction
|
12
|
12
|
15
|
19
|
17
|
7
|
17
|
7
|
14
|
19
|
13.9
|
9
|
Fetal hydrops
|
32
|
16
|
19
|
4
|
20
|
18
|
20
|
21
|
6
|
15
|
17.1
|
10
|
Infant rank
|
11
|
15
|
7
|
11
|
7
|
23
|
7
|
15
|
59
|
31
|
18.6
|
11
|
Amniotic fluid
|
17
|
10
|
18
|
22
|
18
|
8
|
18
|
8
|
42
|
27
|
18.8
|
12
|
Ghypertension
|
9
|
14
|
10
|
28
|
10
|
21
|
16
|
25
|
10
|
59
|
20.2
|
13
|
Infertility
|
13
|
13
|
8
|
12
|
8
|
17
|
8
|
19
|
57
|
48
|
20.3
|
14
|
Decollement / Placenta abruption
|
27
|
11
|
20
|
20
|
23
|
30
|
23
|
31
|
4
|
14
|
20.3
|
15
|
Other Chronic disease (Mother)
|
29
|
23
|
26
|
10
|
28
|
28
|
26
|
32
|
5
|
6
|
21.3
|
16
|
Magnesium sulfate
|
3
|
5
|
4
|
6
|
4
|
52
|
4
|
60
|
55
|
46
|
23.9
|
17
|
IUFD
|
48
|
21
|
31
|
16
|
33
|
12
|
31
|
10
|
30
|
9
|
24.1
|
18
|
Hypertension
|
16
|
17
|
14
|
26
|
16
|
20
|
19
|
17
|
40
|
57
|
24.2
|
19
|
Genetic problems/Anomaly
|
26
|
36
|
25
|
15
|
26
|
29
|
23
|
33
|
11
|
25
|
24.9
|
20
|
IUGR
|
20
|
30
|
16
|
27
|
19
|
24
|
21
|
14
|
41
|
38
|
25.0
|
21
|
Vasa Previa
|
41
|
27
|
37
|
7
|
36
|
39
|
35
|
27
|
7
|
4
|
26.0
|
22
|
Surgery
|
28
|
34
|
34
|
31
|
35
|
14
|
34
|
13
|
18
|
23
|
26.4
|
23
|
Cardiac diseases
|
14
|
24
|
22
|
35
|
25
|
11
|
28
|
11
|
52
|
51
|
27.3
|
24
|
ART use
|
37
|
59
|
11
|
23
|
12
|
19
|
12
|
16
|
56
|
30
|
27.5
|
25
|
Cord
|
39
|
19
|
21
|
18
|
21
|
44
|
22
|
28
|
51
|
16
|
27.9
|
26
|
PROM hrs.
|
50
|
9
|
9
|
17
|
14
|
33
|
15
|
30
|
60
|
44
|
28.1
|
27
|
Pulmonary problems
|
34
|
26
|
27
|
9
|
29
|
31
|
25
|
35
|
31
|
35
|
28.2
|
28
|
ART name
|
23
|
22
|
12
|
13
|
9
|
51
|
9
|
49
|
50
|
47
|
28.5
|
29
|
Thick meconium
|
31
|
25
|
30
|
24
|
31
|
27
|
30
|
29
|
37
|
21
|
28.5
|
30
|
Brain diseases
|
56
|
33
|
33
|
29
|
34
|
15
|
33
|
12
|
9
|
43
|
29.7
|
31
|
Digestive diseases
|
38
|
41
|
36
|
30
|
37
|
13
|
37
|
24
|
29
|
33
|
31.8
|
32
|
Placenta Accreta
|
51
|
31
|
28
|
34
|
30
|
32
|
32
|
26
|
28
|
36
|
32.8
|
33
|
PROM
|
24
|
57
|
23
|
37
|
27
|
16
|
29
|
20
|
49
|
52
|
33.4
|
34
|
FHR
|
44
|
8
|
29
|
25
|
11
|
53
|
13
|
54
|
54
|
54
|
34.5
|
35
|
Sex
|
4
|
28
|
35
|
44
|
32
|
23
|
36
|
46
|
43
|
55
|
34.6
|
36
|
Skin diseases
|
53
|
49
|
43
|
33
|
42
|
22
|
41
|
36
|
25
|
3
|
34.7
|
37
|
(Pre) Eclampsia
|
54
|
29
|
24
|
32
|
24
|
46
|
27
|
42
|
16
|
58
|
35.2
|
38
|
Thyroid Disorders
|
19
|
20
|
32
|
36
|
22
|
43
|
24
|
55
|
48
|
53
|
35.2
|
39
|
Eye diseases
|
33
|
32
|
53
|
40
|
52
|
40
|
52
|
37
|
12
|
7
|
35.8
|
40
|
Abortion
|
30
|
43
|
45
|
43
|
44
|
41
|
44
|
48
|
13
|
10
|
36.1
|
41
|
Infectious diseases
|
47
|
35
|
48
|
42
|
46
|
36
|
46
|
40
|
19
|
5
|
36.4
|
42
|
Cancer
|
35
|
38
|
40
|
39
|
39
|
26
|
38
|
23
|
38
|
49
|
36.5
|
43
|
Tumors
|
40
|
40
|
44
|
38
|
43
|
49
|
42
|
44
|
26
|
13
|
37.9
|
44
|
Gdiabetes
|
7
|
51
|
42
|
57
|
41
|
25
|
43
|
18
|
44
|
56
|
38.4
|
45
|
Previa
|
25
|
53
|
39
|
45
|
38
|
35
|
39
|
39
|
45
|
32
|
39.0
|
46
|
Uterus diseases
|
59
|
39
|
41
|
41
|
40
|
34
|
40
|
34
|
33
|
42
|
40.3
|
47
|
Blood problems
|
43
|
37
|
60
|
49
|
59
|
42
|
59
|
45
|
22
|
12
|
42.8
|
48
|
HIV
|
58
|
45
|
47
|
47
|
47
|
48
|
45
|
50
|
20
|
28
|
43.5
|
49
|
Other problems (Fetus)
|
49
|
48
|
55
|
46
|
54
|
37
|
54
|
47
|
34
|
11
|
43.5
|
50
|
Chorioamnionitis
|
15
|
58
|
58
|
58
|
56
|
38
|
55
|
53
|
27
|
26
|
44.4
|
51
|
Diabetes
|
55
|
56
|
46
|
56
|
45
|
59
|
47
|
57
|
15
|
18
|
45.4
|
52
|
Cardiac problems
|
36
|
54
|
50
|
48
|
48
|
57
|
48
|
52
|
24
|
37
|
45.4
|
53
|
Blood diseases
|
18
|
47
|
59
|
59
|
58
|
47
|
58
|
38
|
36
|
34
|
45.4
|
54
|
Maternal age
|
21
|
60
|
38
|
60
|
60
|
60
|
60
|
22
|
53
|
22
|
45.6
|
55
|
Mental diseases
|
57
|
52
|
56
|
53
|
55
|
50
|
57
|
43
|
8
|
29
|
46.0
|
56
|
Respiratory diseases
|
42
|
44
|
57
|
50
|
57
|
54
|
56
|
41
|
21
|
41
|
46.3
|
57
|
Liver diseases
|
45
|
46
|
52
|
55
|
51
|
56
|
51
|
59
|
32
|
17
|
46.4
|
58
|
Brain problem
|
46
|
42
|
54
|
51
|
53
|
55
|
53
|
56
|
23
|
40
|
47.3
|
59
|
Autoimmune
|
60
|
55
|
49
|
52
|
49
|
45
|
49
|
51
|
35
|
29
|
47.4
|
60
|
Kidney diseases
|
52
|
50
|
51
|
54
|
50
|
58
|
50
|
58
|
39
|
50
|
51.2
|
Averaged rank: (r1 + r2 + … + r10) / 10
According to Table 4, GA is the most important risk factor based on implementation of all FS algorithms. Also, the averaged rank of the "maternal kidney disease" variable was the lowest, suggesting that it is the least important risk factor. The variables have been sorted based on the averaged rank, then, 20 feature subsets were created, including 1, 2, …,10,15,20,25,30,35,40,45,50,55 and 60 important variables respectively. According to these subsets, 20 data subsets were created, and ML algorithms were implemented on these data subsets. Figures 2 and 3 reveal the results related to implementing the ML algorithms for 20 data subsets obtained from FS.
According to Figs. 2 and 3, MLP using 15 first important variables with accuracy of 90.86% and F-measure of 90.8% has achieved the best results. The first 15 most important variables are gestational age, delivery type, presentation, steroid administration, macrosomia, prenatal care, infant number and rank, mother addiction, maternal chronic disease history, fetal hydrops, amniotic fluid, gestational hypertension, infertility and placental abruption.
Table 5 provides the best results of every algorithm. Comparing Fig. 1 and Table 5, it is found that use of FS algorithms has caused increased accuracy and F-measure by 4.7% and 4.9% respectively on average.
Table 5
The best performance of each ML methods on various feature subsets
ML Method
|
Accuracy
|
F-measure
|
No of selected features
|
MLP
|
90.86
|
90.8
|
15
|
J48
|
90.29
|
90.2
|
6
|
RF
|
90.24
|
90.3
|
3
|
SVM
|
90.76
|
90.8
|
9
|
NB
|
89.37
|
89
|
15
|
Graphical user interface of the proposed CDSS was designed based on the best algorithm in Visual Studio 2015 and shown in Fig. 4.