Introduction
Theoretical background
Machine learning
Types of machine learning
Maternal complications
Study methodology
Search strategy
Inclusion and exclusion
Data extraction and analysis
Analysis of extracted data
Topical association
Publication profile
Name of the academic journal | Reviewed Article | Focus |
---|---|---|
Procedia Computer Science | Information Systems, health informatics | |
BMC Pregnancy and Childbirth | Pregnancy and childbirth | |
American Journal of Obstetrics and Gynecology | Obstetrics and gynecology | |
Gynecologic and Obstetric Investigation | [38] | Obstetrics and gynecology |
Journal of Investigative Medicine | [39] | Medical research |
Ultrasound in Obstetrics and Gynecology | [40] | Medical research |
BMJ Open | [41] | Medical research |
Neural Computing and Applications | [42] | Neural computing |
PloS one | [43] | Science, engineering and medicine |
Computer Methods and Programs in Biomedicine | Biomedical informatics, medical research | |
Journal of Basic Research in Medical Sciences | [46] | Medical research |
Study objectives
Scope | Study Objective | Ref | Frequency |
---|---|---|---|
Predicting pregnancy risks/complications | Predicting risk level during pregnancy | [33] | |
Explore risks related to voluntary termination of pregnancy | [47] | ||
Prediction of preterm/extreme preterm birth | 9 (35%) | ||
Prediction of risk of uterine rupture | [37] | ||
Prediction of risk of perinatal death | [52] | ||
Exploring pregnancy factors | Determining factors related to successful vaginal delivery | [35] | |
To explore factors responsible for emergency cesarean section | [39] | ||
To Determine influential factors in child mortality prediction | [44] | 7 (27%) | |
To explore factors responsible for preterm birth | |||
Prediction of low birth weight and factors responsible for it | |||
Predicting mode of delivery | To predict delivery method | [8] | 4 (15%) |
To predict success of vaginal birth after cesarean delivery | |||
Predicting outcome of IVF treatment | Predicting early pregnancy loss | [45] | |
Predicting successful pregnancy after IVF | [42] | 3 (11%) | |
Predicting the live birth chance | [55] | ||
Predicting labor outcome | To determine the suitability of induction of labor | [34] | 2 (8%) |
To determine potential value of cervical length in predicting progress of labor | [40] | ||
Comparison between two birth weight groups | To compare outcome of vaginal intended breech deliveries between low weight group and high weight group | [43] | 1 (4%) |
Data profiling
ML algorithms used
Algorithm | Reference |
---|---|
Decision Tree (DT) | |
Logistic Regression (LR) | |
Generalized Linear Model (GLM) | |
K Means Cluster (KMC) | [48] |
Support Vector Machine (SVM) | |
J48 | |
Naïve Bayes (NB) | |
PART | [44] |
Multivariate Analysis (MA) | |
C5.0 Decision Tree (DT) | [9] |
Random Forest (RF) | |
XgBoost (XB) | |
Balanced Random Forest (BRF), AdaBoost Ensemble (AE), Gradient Boosting (GB) | [36] |
K Nearest Neighbors (KNN) | |
C4.5 Decision Tree (DT) | |
Clustering PAM | [53] |
Univariate Analysis (UA) | |
Random Tree (RT), Decision Table | [46] |
Neural Network (NN) | |
Recurrent Neural Network | [49] |
Back Propagation Neural Network (BPNN) | [45] |
Classification And Regression Trees (CART) | [42] |
Multilayer Perceptron Neural Networks (MLP) | [42] |
Study Objectives | Feature Category | Algorithms |
---|---|---|
To predict delivery method | Demographic factors, obstetric characteristics, maternal factors | DT, NB, SVM, GLM |
To compare maternal and neonatal outcome of vaginal intended breech deliveries between low weight group high weight group | MA | |
To predict success of vaginal birth after cesarean delivery | Demographic factors, obstetric characteristics, maternal factors, medical and obstetric history, neonatal features, ultrasound measurements, behavioral parameters | MA, UA, RF, AE, GB |
Prediction of preterm/extreme preterm birth | Demographic factors, obstetric characteristics, maternal factors, current medical record, medical and obstetric history | DT, NN, RF, KNN |
Predicting risk level during pregnancy | Demographic factors, obstetric characteristics, maternal factors, medical and obstetric history | C4.5 DT |
Explore risks related to voluntary termination of pregnancy | Demographic factors, obstetric characteristics, medical and obstetric history, pregnancy termination attributes | DT, GLM, SVM |
Prediction of risk of uterine rupture | Demographic factors, obstetric characteristics, maternal factors, medical and obstetric history | LR |
Prediction of risk of perinatal death | Demographic factors, obstetric characteristics, maternal factors, behavioral parameters | LR, MA, UA |
To explore factors responsible for preterm birth | Demographic factors, obstetric characteristics, maternal factors, behavioral parameters, medical and obstetric history, current medical record | NB, SVM, NN, C5.0 DT, clustering PAM |
Prediction of low birth weight and factors responsible for it | DT, SVM, RF, NB, NN, LR, J48 | |
Determining factors related to successful vaginal delivery | Demographic factors, obstetric characteristics, maternal factors, medical and obstetric history | MA |
To explore factors responsible for emergency cesarean section | Demographic factors, obstetric characteristics, maternal factors, medical and obstetric history, neonatal features | MA, UA |
Predicting successful pregnancy after IVF | Demographic factors, maternal factors, medical and obstetric history, ultrasound measurements | SVM, C4.5, RF, CART |
Predicting early pregnancy loss during IVF treatment | LR, SVM, DT, BPNN, XB, RF | |
Predicting the live birth chance after IVF treatment | SVM, RF, LR, XB | |
To determine the suitability of induction of labor | Demographic factors, maternal factors, medical and obstetric history, ultrasound measurements | MA |
To determine potential value of cervical length in predicting progress of labor | MA, UA |
Study context
Context | Reference | Scope | Features | |
---|---|---|---|---|
North America | USA | Predicting pregnancy risks/complications, mode of delivery | Demographic factors, maternal factors, obstetric characteristics, medical and obstetric history, neonatal features | |
Europe | Portugal | Predicting pregnancy risks/complications, mode of delivery | Demographic factors, maternal factors, obstetric characteristics, pregnancy termination attributes, medical and obstetric history | |
Germany | [43] | Comparison between two birth weight groups | Demographic factors, medical and obstetric history, ultrasound measurements | |
London | [40] | Predicting labor outcome | Demographic factors, medical and obstetric history, ultrasound measurements | |
Timisoara | [53] | Exploring pregnancy factors | Demographic factors, maternal factors, obstetric characteristics, behavioral parameters, current medical record | |
Scotland | [52] | Predicting pregnancy risks/complications | Demographic factors, maternal factors, obstetric characteristics, behavioral parameters | |
Slovenia | [50] | Predicting pregnancy risks/complications | Demographic factors, maternal factors, obstetric characteristics, EHG related features | |
South Asia | India | Predicting pregnancy risks/complications | Demographic factors, maternal factors, obstetric characteristics, medical and obstetric history, behavioral parameters, current medical record | |
East Asia | China | Exploring pregnancy factors, predicting mode of delivery and outcome of IVF treatment | Demographic factors, maternal factors, obstetric characteristics, medical and obstetric record, ultrasound measurements, neonatal features, infertility characteristics | |
Taiwan | [9] | Exploring pregnancy factors | Demographic factors, maternal factors, obstetric characteristics, behavioral parameters, medical and obstetric history | |
Middle East | Iran | Predicting outcome of labor, exploring pregnancy factors | Demographic factors, maternal factors, obstetric characteristics, medical and obstetric history, ultrasound characteristics | |
Turkey | [42] | Predicting outcome of IVF treatment | Demographic factors, maternal factors, obstetric characteristics, infertility characteristics | |
Israel | [36] | Predicting mode of delivery | Demographic factors, medical and obstetric history, obstetric characteristics, behavioral parameters, neonatal factors | |
East Africa | Ethiopia | Exploring pregnancy factors | Demographic factors, obstetric characteristics, maternal factors, medical and obstetric history | |
Australia | - | [10] | Predicting pregnancy risks/complications | Demographic factors, obstetric characteristics, medical and obstetric history |