Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy - Eduardo Alonso; Andoni Beristain; Jorge Burgos; Ibai Gurrutxaga (Applied Sciences)
This paper presents a novel approach for screening women in their first trimester of pregnancy to identify those at high risk of having a child with Down syndrome (DS), using machine learning algorithms. Various machine learning models, including statistical, linear, and ensemble models, were trained using a pseudo-anonymized dataset of 90,532 screening patients. This dataset, containing less than 1% positive cases, was obtained from Cruces University Hospital, a public health hospital (Osakidetza) in Baracaldo, Basque Country, Spain. The models incorporate a set of input variables, including demographic variables such as maternal age, weight, ethnicity, smoking status, and diabetes status, as well as laboratory variables like nuchal translucency (NT), pregnancy-associated plasma protein-A (PAPP-A), and beta-human chorionic gonadotropin hormone (B-HCG) levels. The trained classification algorithms achieved ROC-AUC values between 0.970 and 0.982, with sensitivity and specificity of 0.94. The results indicate that machine learning techniques can effectively predict Down syndrome risk in first-trimester screening programs.