Discriminating Healthy and IUGR fetuses through Machine Learning models

The purpose of this study is to develop and understand whether Machine Learning models can classify Cardiotocographic (CTG) recordings of healthy fetuses or Intra Uterine Growth Restricted (IUGR) fetuses, highlighting how a large amount of data can have unexpected effects. We started from other findings in the literature to see what Machine Learning model remained consistent even with a large amount of data. The CTG records used in this study were collected at the Department of Obstetrics of the Federico II University Hospital in Naples, Italy, from 2013 to 2021. From this dataset, we chose 1548 IUGR fetuses and 1548 healthy fetuses to train our models. Each recording contained several parameters, ranging from features calculated on the entire CTG tracing, features calculated every 3 and 1 minute of recording and features related to the pregnant woman, such as age and week of gestation. We trained our machine-learning models on this dataset, checking the results obtained before and after adjusting the hyperparameters, noting that among the best models was Random Forest, which has already been present in other studies, and that the Multilayer Perceptron and the AdaBoost classifier were overall the best performing. This work can surely form a basis for future works in the fetal heart rate classification thus leading to real clinical applications.