A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective
This study demonstrates that decision tree models are effective tools for stratifying the risk of MTCT of HBV by integrating key clinical and paraclinical markers. Among these, HBeAg status and PBMC concentration emerged as the most critical predictors. While the analysis focused on untreated patients, it provides a strong foundation for future investigations involving treated populations. These findings offer actionable insights to support the development of more targeted and effective HBV MTCT prevention strategies.
Authors
- Ho Cam Tu (ORCID: https://orcid.org/0000-0001-8239-096X)
- Huong Thi Thu Bui (ORCID: https://orcid.org/0000-0002-4101-5618)
- Lam Khanh Ho
- Lan Thu Hoang
- Huyen Nguyen Thi Thu (ORCID: https://orcid.org/0009-0004-0571-2370)
- Thuy Thi Pham (ORCID: https://orcid.org/0000-0002-4140-7603)
- Thi Lan Anh Luong (ORCID: https://orcid.org/0000-0003-0669-9919)
- Nina Körber (ORCID: https://orcid.org/0000-0002-0991-6547)
- Tanja Bauer
- Dũng Nguyễn Tiến (ORCID: https://orcid.org/0009-0000-6394-1025)
- Thi-Hanh-Thu Vu
- Tram Hoang Thi Ngoc
- Duy‐Hieu Nguyen
Institutions
- Hung Yen University of Technology and Education (VN)
- Hanoi Medical University (VN)
- Thai Nguyen National General Hospital (VN)
- Helmholtz Zentrum München (DE)
- Thai Nguyen University (VN)
- Hanoi Medical University Hospital (VN)
- Technical University of Munich (DE)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-05-23
- DOI
- https://doi.org/10.2196/69838
- Citations
- 2
- Primary Topic
- Hepatitis B Virus Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.71