Prediction of Late Intrauterine Growth Restriction using Machine Learning Models

Abstract

Late IUGR is associated with a higher risk of perinatal hypoxic events and suboptimal neurodevelopment and is a leading cause of perinatal mortality and usually suspected in the third trimester of pregnancy, eventually confirmed at birth. As IUGR in the third trimester of pregnancy is greatly associated with unexplained stillbirths in low-risk pregnancies, prompt antenatal diagnosis and treatment with timely delivery could significantly reduce these risks. In this study, we assessed the presence of late IUGR using CTG findings. We applied 7 machine learning models (Naïve Bayes, k-Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest and AdaBoost) under three different experiments highlighting the effect of pre-processing techniques. The best performing models are obtained by logistic regression and support vector machine with accuracy rates of 84-85%, precision of 79-80%, recall at 85-89% and F-scores of 82-84%. The models perform exceptionally well in all evaluation metrics, showing robustness and flexibility as a predictive model for the late IUGR. Based on the results of our experiments, we stressed the importance of feature elimination as a pre-processing technique to improve model performance. Through feature importance method, we also identified the top relevant features in predicting late IUGR for both logistic regression and support vector machine.