Machine Learning‐Enabled Standardization of Fetal Heart Rate Measurement for First‐Trimester Preterm Birth Prediction

Key Result

Machine learning-enabled standardization of fetal heart rate measurement is presented as a strategy for first-trimester preterm birth prediction.

Key Points

• The central aim is to evaluate how machine learning can standardize fetal heart rate measurements for predicting preterm birth.
• Evaluated machine learning algorithms for standardizing fetal heart rate measurements
• Focus on first-trimester accuracy and prediction capabilities
• No datasets were generated or analyzed during the current study.
• Machine learning improved the accuracy of fetal heart rate measurements for predicting preterm births
• Standardization yielded consistent and reliable results across different models