Objective: Intrauterine growth restriction (IUGR) is one of the most common causes of stillbirths. The objective of this study is to develop a machine learning model that will be able to accurately and consistently predict whether the estimated fetal weight (EFW) will be below the 10th percentile at 34+0-37 + 6 week's gestation stage, by using data collected at 20 + 0 to 23 + 6 weeks gestation.
Methods: Recruitment for the prospective Safe Passage Study (SPS) was done over 7.5 years (2007-2015). An essential part of the fetal assessment was the non-invasive transabdominal recording of the maternal and fetal electrocardiograms as well as the performance of an ultrasound examination for Doppler flow velocity waveforms and fetal biometry at 20 + 0 to 23 + 6 and 34 + 0 to 37 + 6 week's gestation. Several predictive models were constructed, using supervised learning techniques, and evaluated using the Stochastic Gradient Descent, k-Nearest Neighbours, Logistic Regression and Random Forest methods.
Results: The final model performed exceptionally well across all evaluation metrics, particularly so for the Stochastic Gradient Descent method: achieving a 93% average for Classification Accuracy, Recall, Precision and F1-Score when random sampling is used and 91% for cross-validation (both methods using a 95% confidence interval). Furthermore, the model identifies the Umbilical Artery Pulsality Index to be the strongest identifier for the prediction of IUGR - matching the literature. Three of the four evaluation methods used achieved above 90% for both True Negative and True Positive results. The ROC Analysis showed a very strong True Positive rate (y-axis) for both target attribute outcomes - AUC value of 0.771.
Conclusions: The model performs exceptionally well in all evaluation metrics, showing robustness and flexibility as a predictive model for the binary target attribute of IUGR. This accuracy is likely due to the value added by the pre-processed features regarding the fetal gained beats and accelerations, something otherwise absent from previous multi-disciplinary studies. The success of the proposed predictive model allows the pursuit of further birth-related anomalies, providing a foundation for more complex models and lesser-researched subject matter. The data available for this model was a vital part of its success but might also become a limiting factor for further analyses. Further development of similar models could result in better classification performance even with little data available.
Keywords: Classification; Fetal heart rate accelerations; IUGR; Machine learning; Umbilical artery Doppler.