Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy

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Abstract
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.
Funding Information
  • Spanish Ministry of Science, Innovation and Universities—National Research Agency (PID2021-123087OB-I00, IT-1437-22)
  • European Regional Development Fund (ERDF) (PID2021-123087OB-I00, IT-1437-22)
  • Department of Education, Universities and Research of the Basque Government (PID2021-123087OB-I00, IT-1437-22)

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