Do, Huy DAllison, Jeroan J.Nguyen, Hoa LPhung, Hai NTran, Cuong DLe, Giang MNguyen, Trang T2024-09-132024-09-132024-07-15Do HD, Allison JJ, Nguyen HL, Phung HN, Tran CD, Le GM, Nguyen TT. Applying machine learning in screening for Down Syndrome in both trimesters for diverse healthcare scenarios. Heliyon. 2024 Jul 15;10(15):e34476. doi: 10.1016/j.heliyon.2024.e34476. PMID: 39144940; PMCID: PMC11320142.2405-844010.1016/j.heliyon.2024.e3447639144940https://hdl.handle.net/20.500.14038/53777Background: This paper describes the development of low-cost, effective, non-invasive machine learning-based prediction models for Down Syndrome in the first two trimesters of pregnancy in Vietnam. These models are adaptable to different situations with limited screening capacities at community-based healthcare facilities. Method: Ultrasound and biochemical testing alone and in combination, from both trimesters were employed to build prediction models based on k-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting algorithms. Results: A total of 7,076 pregnant women from a single site in Northern Vietnam were included, and 1,035 had a fetus with Down Syndrome. Combined ultrasound and biochemical testing were required to achieve the highest accuracy in trimester 2, while models based only on biochemical testing performed as well as models based on combined testing during trimester 1. In trimester 1, Extreme Gradient Boosting produced the best model with 94% accuracy and 88% AUC, while Support Vector Machine produced the best model in trimester 2 with 89% accuracy and 84% AUC. Conclusions: This study explored a range of machine learning models under different testing scenarios. Findings point to the potential feasibility of national screening, especially in settings without enough equipment and specialists, after additional model validation and fine tuning is performed.en© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Journal ArticleHeliyon