Related Experiment Video
Updated: Jan 4, 2026
Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System
Published on: May 5, 2018
Use of Machine Learning Algorithms for Prediction of Fetal Risk using Cardiotocographic Data
Zahra Hoodbhoy1, Mohammad Noman2, Ayesha Shafique2
1Department of Paediatrics and Child Health, The Aga Khan University, Karachi, Pakistan.
Insights
Machine learning accurately identifies high-risk fetuses using cardiotocograph (CTG) data. The XGBoost model shows high precision in predicting pathological fetal states, aiding early intervention for better infant outcomes.
Area of Science:
- Perinatal medicine
- Medical informatics
- Machine learning in healthcare
Background:
- Intrapartum complications are a leading cause of perinatal mortality.
- Fetal cardiotocograph (CTG) monitoring identifies high-risk pregnancies during labor.
- Under-five mortality is significantly impacted by neonatal deaths.
Purpose of the Study:
- To evaluate the precision of machine learning algorithms using CTG data.
- To identify high-risk fetuses with adverse outcomes.
- To improve early detection of fetal distress.
Main Methods:
- Trained ten machine learning models on CTG data from 2126 women.
- Utilized XGBoost, decision tree, and random forest algorithms.
- Assessed model performance using sensitivity, precision, F1 score, and accuracy.
Main Results:
- XGBoost, decision tree, and random forest models achieved >96% precision on training data.
- The XGBoost model demonstrated the highest precision (>92%) for pathological fetal states on testing data.
- Obstetrician interpretation served as the gold standard for fetal states (70% normal, 20% suspect, 10% pathological).
Conclusions:
- The XGBoost model offers high prediction accuracy for adverse fetal outcomes.
- This model can assist lay healthcare workers in low-resource settings for early triage and referral.
- Improved identification of high-risk fetuses can reduce neonatal mortality.
Background:
A major contributor to under-five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor.
Aim:
The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses.
Methods:
CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Ten different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. Model with best performance on specified metrics was then identified.
Results:
Determined by obstetricians' interpretation of CTGs as gold standard, 70% of them were normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification models generated by XGBoost, decision tree, and random forest had high precision (>96%) to predict the suspect and pathological state of the fetus based on the CTG tracings. However, on testing data, XGBoost model had the highest precision to predict a pathological fetal state (>92%).
Conclusion:
The classification model developed using XGBoost technique had the highest prediction accuracy for an adverse fetal outcome. Lay health-care workers in low- and middle-income countries can use this model to triage pregnant women in remote areas for early referral and further management.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
06:56Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
Published on: January 7, 2021