Automated Fetal and Maternal Health Management Using AI
Author Information
Author(s): Innab Nisreen, Alsubai Shtwai, Alabdulqader Ebtisam Abdullah, Alarfaj Aisha Ahmed, Umer Muhammad, Trelova Silvia, Ashraf Imran
Hypothesis
Can a light gradient boosting model effectively classify fetal health using cardiotocography data?
Conclusion
The proposed model achieved high accuracy and performance metrics, demonstrating its effectiveness in classifying fetal health.
Supporting Evidence
- The model achieved 99.89% accuracy and 99.88% AUC on the test dataset.
- SMOTE was used to address class imbalance in the dataset.
- The proposed model outperformed several other machine learning models in fetal health classification.
Takeaway
This study created a smart computer program that helps doctors check if babies are healthy before they are born by looking at heart rate data.
Methodology
The study used a light gradient boosting machine and synthetic minority oversampling technique (SMOTE) to classify fetal health based on cardiotocography data.
Potential Biases
Potential biases may arise from the dataset not being representative of diverse populations.
Limitations
The study may have biases due to the dataset's class imbalance and the reliance on specific machine learning models.
Participant Demographics
The dataset includes records from expecting mothers with gestational ages between 29 and 42 weeks.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website