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Multi-Task Machine Learning for Prenatal Risk Stratification: Integrating Biomarkers, Maternal Age, and Ultrasound Measurements to Predict the Risk of Down Syndrome, Trisomy 18, Trisomy 13, and Neural Tube Defects
Research Article | Published: 06 July 2025
Frontiers in Biomedical Signal Processing Volume 1, Issue 1, 2025: 24-36
Volume 1, Issue 1, Frontiers in Biomedical Signal Processing
Volume 1, Issue 1, 2025
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Frontiers in Biomedical Signal Processing, Volume 1, Issue 1, 2025: 24-36

Open Access | Research Article | 06 July 2025
Multi-Task Machine Learning for Prenatal Risk Stratification: Integrating Biomarkers, Maternal Age, and Ultrasound Measurements to Predict the Risk of Down Syndrome, Trisomy 18, Trisomy 13, and Neural Tube Defects
by
Seyed-Ali Sadegh-Zadeh Seyed-Ali Sadegh-Zadeh 1
Alireza Soleimani Mamalo Alireza Soleimani Mamalo 2 *
Shayan Saadat Shayan Saadat 3
Sahar Sayyadi Gargari Sahar Sayyadi Gargari 2
Mohammad Amin Barati Mohammad Amin Barati 4
Sahar Mehranfar Sahar Mehranfar 4
Zahra Naderi Zahra Naderi 5
1 Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent ST4 2DE, United Kingdom
2 Student Research Committee, Urmia University of Medical Sciences, Urmia‚ Iran
3 Hull York Medical School, University of York, York, United Kingdom
4 School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
5 Department of Genetics and Immunology, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran
* Corresponding Author: Alireza Soleimani Mamalo, [email protected]
DOI: 10.62762/FBSP.2025.954863
Received: 31 March 2025, Accepted: 31 May 2025, Published: 06 July 2025  
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Abstract
This study developed a machine learning model for early risk stratification of Down syndrome by integrating maternal serum biomarkers and ultrasound measurements. A retrospective multicentre dataset was used, including maternal age, AFP, HCG, INHIBIN-A, and ultrasound parameters (NT, CRL). After imputing missing data and engineering features (e.g., Age_NT_interaction), a Gradient Boosting Machine (GBM) was trained and evaluated using AUROC, precision, recall, and F1-score. The model achieved high performance (AUROC: 0.9921; precision: 1.00; F1-score: 0.91; accuracy: 0.97). SHAP analysis identified key interactions—particularly Age_NT, Age_HCG, and Age_PAPP-A—as major contributors. High maternal age combined with elevated HCG or low PAPP-A was linked to increased risk, aligning with clinical knowledge. The model offers a highly accurate and interpretable approach for Down syndrome risk prediction, supporting personalized, data-driven prenatal care. Prospective validation and clinical integration are recommended.
Graphical Abstract
Multi-Task Machine Learning for Prenatal Risk Stratification: Integrating Biomarkers, Maternal Age, and Ultrasound Measurements to Predict the Risk of Down Syndrome, Trisomy 18, Trisomy 13, and Neural Tube Defects
Keywords
down syndrome
machine learning
prenatal screening
SHAP analysis
maternal biomarkers
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, Seyed-Ali Sadegh-Zadeh, upon reasonable request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
This study was conducted in accordance with the ethical principles and national norms and standards for conducting medical research in Iran, as approved by the Research Ethics Committee of Urmia University of Medical Sciences (Approval ID: IR.UMSU.REC.1403.234, Approval Date: 2024-10-30). Written informed consent was obtained from all participants. The researchers ensured compliance with all professional and legal requirements, maintaining the confidentiality and anonymity of participant data.
References
  1. Abedalthagafi, M., Bawazeer, S., Fawaz, R. I., Heritage, A. M., Alajaji, N. M., & Faqeih, E. (2023). Non-invasive prenatal testing: a revolutionary journey in prenatal testing. Frontiers in Medicine, 10, 1265090.
    [CrossRef]   [Google Scholar]
  2. Aboughalia, H., Bastawrous, S., Revzin, M. V., Delaney, S. S., Katz, D. S., & Moshiri, M. (2020). Imaging findings in association with altered maternal alpha-fetoprotein levels during pregnancy. Abdominal Radiology, 45, 3239–3257.
    [Google Scholar]
  3. Aprigio, J., de Castro, C. M., Lima, M. A. C., Ribeiro, M. G., Orioli, I. M., & Amorim, M. R. (2023). Mothers of children with Down syndrome: A clinical and epidemiological study. Journal of Community Genetics, 14(2), 189-195.
    [CrossRef]   [Google Scholar]
  4. Chaemsaithong, P., Sahota, D. S., & Poon, L. C. (2022). First trimester preeclampsia screening and prediction. American journal of obstetrics and gynecology, 226(2), S1071-S1097.
    [CrossRef]   [Google Scholar]
  5. Colnet, B., Josse, J., Varoquaux, G., & Scornet, E. (2023). Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize? ArXiv Preprint ArXiv:2303.16008.
    [CrossRef]   [Google Scholar]
  6. de Souza Lima, B., Sanches, A. P. V., Ferreira, M. S., de Oliveira, J. L., Cleal, J. K., & Ignacio-Souza, L. (2024). Maternal-placental axis and its impact on fetal outcomes, metabolism, and development. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1870(1), 166855.
    [CrossRef]   [Google Scholar]
  7. Demir, S., & Sahin, E. K. (2023). An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost. Neural Computing and Applications, 35(4), 3173-3190.
    [CrossRef]   [Google Scholar]
  8. Esbensen, A. J., Schworer, E. K., & Hartley, S. L. (2024). Down syndrome. Intellectual and Developmental Disabilities: A Dynamic Systems Approach, 279-302.
    [CrossRef]   [Google Scholar]
  9. Asselman, A., Khaldi, M., & Aammou, S. (2023). Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 31(6), 3360-3379.
    [CrossRef]   [Google Scholar]
  10. Keilty, B., Jackson, M. A., & Smith, J. (2024). Families’ experiences with supports after receiving a prenatal diagnosis of down syndrome. Early Childhood Research Quarterly, 66, 1-10.
    [CrossRef]   [Google Scholar]
  11. Leung, C., Su, L., Simões-e-Silva, A. C., Arocha, L. S., de Paiva, K. M., & Haas, P. (2023). Risk for severe illness and death among pediatric patients with down syndrome hospitalized for COVID-19, Brazil. Emerging Infectious Diseases, 29(1), 26.
    [CrossRef]   [Google Scholar]
  12. Poynard, T., Halfon, P., Castera, L., Charlotte, F., Le Bail, B., Munteanu, M., ... & Bourlière, M. (2007). Variability of the area under the receiver operating characteristic curves in the diagnostic evaluation of liver fibrosis markers: impact of biopsy length and fragmentation. Alimentary Pharmacology & Therapeutics, 25(6), 733–739.
    [Google Scholar]
  13. Rose, N. C., Kaimal, A. J., Dugoff, L., Norton, M. E., & American College of Obstetricians and Gynecologists. (2020). Screening for fetal chromosomal abnormalities: ACOG practice bulletin, number 226. Obstetrics & Gynecology, 136(4), e48–e69.
    [Google Scholar]
  14. Cabello-Solorzano, K., Ortigosa de Araujo, I., Peña, M., Correia, L., & J. Tallón-Ballesteros, A. (2023, August). The impact of data normalization on the accuracy of machine learning algorithms: a comparative analysis. In International conference on soft computing models in industrial and environmental applications (pp. 344-353). Cham: Springer Nature Switzerland.
    [CrossRef]   [Google Scholar]
  15. Sarker, M. (2024). Revolutionizing healthcare: the role of machine learning in the health sector. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), 36-61.
    [CrossRef]   [Google Scholar]
  16. Zhang, Y., Zheng, Y., Wang, D., Gu, X., Zyphur, M. J., Xiao, L., ... & Deng, Y. (2025). Shedding Light on the Black Box: Integrating Prediction Models and Explainability Using Explainable Machine Learning. Organizational Research Methods, 10944281251323248.
    [CrossRef]   [Google Scholar]
  17. Sadegh-Zadeh, S. A., Nazari, M. J., Aljamaeen, M., Yazdani, F. S., Mousavi, S. Y., & Vahabi, Z. (2024). Predictive models for Alzheimer's disease diagnosis and MCI identification: The use of cognitive scores and artificial intelligence algorithms. NPG Neurologie-Psychiatrie-Gériatrie, 24(142), 194-211.
    [CrossRef]   [Google Scholar]
  18. Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Rab, S. (2022). Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks, 3, 58-73.
    [CrossRef]   [Google Scholar]
  19. Sadegh-Zadeh, S. A., Sakha, H., Movahedi, S., Harandi, A. F., Ghaffari, S., Javanshir, E., ... & Hajizadeh, R. (2023). Advancing prognostic precision in pulmonary embolism: a clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification. Computers in Biology and Medicine, 167, 107696.
    [CrossRef]   [Google Scholar]
  20. Sadegh-Zadeh, S. A., Soleimani Mamalo, A., Kavianpour, K., Atashbar, H., Heidari, E., Hajizadeh, R., ... & Gargari, S. S. (2024). Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions. Frontiers in Artificial Intelligence, 7, 1381455.
    [CrossRef]   [Google Scholar]
  21. Steffensen, E. H., Pedersen, L. H., Lou, S., Vogel, I., Danish Fetal Medicine Study Group, & Danish Cytogenetic Central Registry Study Group. (2023). Is the first‐trimester combined screening result associated with the phenotype of Down syndrome? A population‐based cohort study. Prenatal Diagnosis, 43(1), 51-61.
    [CrossRef]   [Google Scholar]
  22. Valentini, D., Di Camillo, C., Mirante, N., Vallogini, G., Olivini, N., Baban, A., ... & Villani, A. (2021). Medical conditions of children and young people with Down syndrome. Journal of Intellectual Disability Research, 65(2), 199-209.
    [CrossRef]   [Google Scholar]
  23. Wang, Q., Ma, Y., Zhao, K., & Tian, Y. (2022). A comprehensive survey of loss functions in machine learning. Annals of Data Science, 9(2), 187-212.
    [CrossRef]   [Google Scholar]
  24. Yang, Y., & Xu, Z. (2020). Rethinking the value of labels for improving class-imbalanced learning. Advances in Neural Information Processing Systems, 33, 19290–19301.
    [CrossRef]   [Google Scholar]
  25. Zhang, J., Ma, X., Zhang, J., Sun, D., Zhou, X., Mi, C., & Wen, H. (2023). Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. Journal of environmental management, 332, 117357.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Sadegh-Zadeh, S. A., Mamalo, A. S., Saadat, S., Gargari, S. S., Barati, M. A., Mehranfar, S., & Naderi, Z. (2025). Multi-Task Machine Learning for Prenatal Risk Stratification: Integrating Biomarkers, Maternal Age, and Ultrasound Measurements to Predict the Risk of Down Syndrome, Trisomy 18, Trisomy 13, and Neural Tube Defects. Frontiers in Biomedical Signal Processing, 1(1), 24–36. https://doi.org/10.62762/FBSP.2025.954863
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CC BY Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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