ArticleJMIR formative research2025
A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The blocking efficacy and safety of tenofovir disoproxil fumarate tablets on mother-to-child transmission in pregnant women with high serum HBV DNA load: a retrospective cohort study.BMC pregnancy and childbirth · 2025Article
- Maximizing Diagnostic Yield in Intellectual Disability Through Exome Sequencing: Genotype-Phenotype Insights in a Vietnamese Cohort.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatitis B virus (HBV) can be transmitted from mother to child either through transplacental infection or via blood-to-blood contact during or immediately after delivery. Early and accurate risk assessments are essential for guiding clinical decisions and implementing effective preventive measures. Data mining techniques are powerful tools for identifying key predictors in medical diagnostics.
objectiveThis study aims to develop a robust predictive model for mother-to-child transmission (MTCT) of HBV using decision tree algorithms, specifically Iterative Dichotomiser 3 (ID3) and classification and regression trees (CART). The study identifies clinically and paraclinically relevant predictors, particularly hepatitis B e antigen (HBeAg) status and peripheral blood mononuclear cell (PBMC) concentration, for effective risk stratification and prevention. Additionally, we will assess the model's reliability and generalizability through cross-validation with various training-test split ratios, aiming to enhance its applicability in clinical settings and inform improved preventive strategies against HBV MTCT.
methodsThis study used decision tree algorithms-ID3 and CART-on a data set of 60 hepatitis B surface antigen (HBsAg)-positive pregnant women. Samples were collected either before or at the time of delivery, enabling the inclusion of patients who were undiagnosed or had limited access to treatment. We analyzed both clinical and paraclinical parameters, with a particular focus on HBeAg status and PBMC concentration. Additional biochemical markers were evaluated for their potential contributory or inhibitory effects on MTCT risk. The predictive models were validated using multiple training-test split ratios to ensure robustness and generalizability.
resultsOur analysis showed that 20 out of 48 (based on a split ratio of 0.8 from a total of 60 cases, 42%) to 27 out of 57 (based on a split ratio of 0.95 from a total of 60 cases, 47%) training cases with HBeAg-positive status were associated with a significant risk of MTCT of HBV (χ
conclusionsThis 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.
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