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Published: 2025-09-30
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DOI: https://doi.org/10.52852/tcncyh.v194i9.3884
Issue
Vol. 194 No. 9 (2025)
Section
Các bài báo
How to Cite
Hiếu, Đặng H., Trung, V. Q., Phong, V. N. H., Thơ, N. N., Mạnh, T. P., & Trang, N. T. (2025). 67. Application of machine learning models to analyze thrombophilia-related gene polymorphisms in recurrent miscarriage. Journal of Medical Research, 194(9), 667-677. https://doi.org/10.52852/tcncyh.v194i9.3884

67. Application of machine learning models to analyze thrombophilia-related gene polymorphisms in recurrent miscarriage

Dang Huy Hieu, Vu Quoc Trung, Vu Nguyen Hong Phong, Nguyen Ngoc Tho, Tran Pham Manh, Nguyen Thi Trang

Main Article Content

Abstract

Recurrent miscarriage is a serious obstetric complication that has been associated with thrombophilic gene mutations. However, manual analysis of gene combinations is not feasible due to the large number of genes involved. Therefore, we conducted a study using the Multifactor Dimensionality Reduction (MDR) machine learning model to analyze genetic data and identify gene combinations associated with the risk of recurrent miscarriage in women with a history of pregnancy loss. This is a retrospective cross-sectional study conducted at the Department of Biomedical Genetics, Hanoi Medical University, and the Institute of DNA Technology and Genetic Analysis – Genlab. Analysis of nine gene combinations revealed that individuals with homozygous PAI-1 4G/5G mutations and heterozygous MTR A2756G variants had a 9.36 times increased risk of recurrent miscarriage. This two-gene combination yielded a predictive accuracy of 61.31% and a cross-validation consistency of 8 out of 10. The study highlights the potential of MDR in predicting gene combinations associated with an increased risk of recurrent miscarriage, thereby supporting appropriate counseling and treatment strategies.

Article Details

Keywords

MDR, recurrent miscarriage, thrombophilic gene, SERPINE1-675 4G/5G, MTR c.2756A>G

References

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