Table 2.
Machine learning algorithm and category of outcome.
| Variable | Number of studies (percentage based on column total) | ||||
|
|
LRa (n=77), n (%) | Non-LR (n=50), n (%) | Both (n=15), n (%) | Total (n=142), n (%) | |
| Machine learning algorithm | |||||
|
|
Logistic regression | 77 (100) | N/Ab | 15 (100) | 92 (64.8) |
|
|
Artificial neural network | N/A | 15 (30) | 5 (33) | 20 (14.1) |
|
|
Support vector machine | N/A | 9 (18) | 1 (7) | 10 (7.0) |
|
|
Deep neural network | N/A | 8 (16) | 1 (7) | 9 (6.3) |
|
|
Random forest | N/A | 7 (14) | 1 (7) | 8 (5.6) |
|
|
Decision tree | N/A | 2 (4) | 5 (33) | 7 (4.9) |
|
|
Gradient boosting | N/A | 3 (6) | 2 (13) | 5 (3.5) |
|
|
Naïve Bayes | N/A | 4 (8) | 0 (0) | 4 (2.8) |
|
|
Ensemble of algorithms | N/A | 2 (4) | 0 (0) | 2 (1.4) |
| Category of outcome | |||||
|
|
Premature birth | 9 (12) | 12 (24) | 3 (20) | 24 (16.9) |
|
|
In vitro fertilization | 7 (9) | 13 (26) | 2 (13) | 22 (15.5) |
|
|
Obstetric labor | 13 (17) | 1 (2) | 2 (13) | 16 (11.3) |
|
|
Pregnancy-induced hypertension | 12 (16) | 4 (8) | 0 (0) | 16 (11.3) |
|
|
Fetal distress | 1 (1) | 9 (18) | 0 (0) | 10 (7.0) |
|
|
Gestational diabetes | 7 (9) | 2 (4) | 1 (7) | 10 (7.0) |
|
|
Cesarean section | 4 (5) | 3 (6) | 2 (13) | 9 (6.3) |
|
|
Fetal development | 4 (5) | 1 (2) | 0 (0) | 5 (3.5) |
|
|
Small-for-gestational-age infant | 3 (4) | 1 (2) | 1 (7) | 5 (3.5) |
|
|
Others | 17 (22) | 4 (8) | 4 (27) | 25 (17.6) |
aLR: logistic regression.
bN/A: not applicable.