Prevalence of thalassemia in the Vietnamese population and building a clinical decision support system for prenatal screening for thalassemia
Danh Cuong Tran 1 2 , Anh Linh Dang 1 , Thi Ngoc Lan Hoang 3 , Chi Thanh Nguyen 4 , Thi Minh Phuong Le 5 , Thi Ngoc Mai Dinh 6 , Van Anh Tran 7 , Thi Kim Phuong Doan 3 , Thi Trang Nguyen 3 8 *
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1 Center for Prenatal Diagnosis, National Hospital of Obstetrics and Gynecology, Hanoi, VIETNAM2 Department of Obstetrics and Gynecology, Hanoi Medical University, Hanoi, VIETNAM3 Department of Biomedical and genetics, Hanoi Medical University, Hanoi, VIETNAM4 Department of Specialized Software, Academy of Military Science and Technology, Hanoi, VIETNAM5 Department of Basic Sciences in Medicine and Pharmacy, University of Medicine and Pharmacy-Vietnam National University, Hanoi, VIETNAM6 Department of Pediatrics, Hanoi Medical University, Hanoi, VIETNAM7 Department of Pediatrics, Hanoi Medical University Hospital, Hanoi, VIETNAM8 Clinical Genetics Center, Hanoi Medical University Hospital, Hanoi, VIETNAM* Corresponding Author


The prevalence of thalassemia among the Vietnamese population was studied, and clinical decision support systems (CDSSs) for prenatal screening of thalassemia were created. A cross-sectional study was conducted on pregnant women and their husbands visiting from October 2020 to December 2021. A total of 10,112 medical records of first-time pregnant women and their husbands were collected. CDSS including two different types of systems for prenatal screening for thalassemia (expert system [ES] and four artificial intelligence [AI]-based CDSS) was built. 1,992 cases were used to train and test machine learning (ML) models while 1,555 cases were used for specialized ES evaluation. There were 10 key variables for AI-based CDSS for ML. The four most important features in thalassemia screening were identified. Accuracy of ES and AI-based CDSS was compared. The rate of patients with alpha thalassemia is 10.73% (1,085 patients), the rate of patients with beta-thalassemia is 2.24% (227 patients), and 0.29% (29 patients) of patients carry both alpha-thalassemia and beta-thalassemia gene mutations. ES showed an accuracy of 98.45%. Among AI-based CDSS developed, multilayer perceptron model was the most stable regardless of the training database (accuracy of 98.50% using all features and 97.00% using only the four most important features). AI-based CDSS showed satisfactory results. Further development of such systems is promising with a view to their introduction into clinical practice.


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Article Type: Original Article

ELECTRON J GEN MED, 2023, Volume 20, Issue 4, Article No: em501

Publication date: 01 Jul 2023

Online publication date: 14 Apr 2023

Article Views: 909

Article Downloads: 1032

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