Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques

The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.