| Title: | Machine Learning-Based Detection of HbS and HbC Carriers in the UK General Population |
| Journal: | eJHaem |
| Published: | 4 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41195239/ |
| DOI: | https://doi.org/10.1002/jha2.70170 |
| URL: | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jha2.70170 |
Publication 19061
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Abstract
Background: Haemoglobin S (HbS) and C (HbC) are the most important sickling variants on the African continent, imposing major health burdens. Early detection of carrier status is crucial but often hindered by resource limitations.</p>
Objectives: To develop machine learning (ML) models to accurately classify HbS and HbC carriers using readily available routine blood tests, facilitating cost-effective mass screening.</p>
Methods: We utilised demographic and routine blood parameters from 469,248 individuals from the UK general population, including 1635 individuals with HbS and/or HbC variants identified by whole exome sequencing, to develop ML models for carrier detection based on standard blood tests. Three ML models (Logistic Regression [LR], Random Forest [RF] and XGBoost [XGB]) were trained using 32 different standard blood test results.</p>
Results: All models demonstrated high discriminatory ability (ROC-AUC: LR 0.951; RF 0.943; XGB 0.956) in the UK general population. At a sensitivity of 95%, specificities were 77% (LR), 76% (RF) and 78% (XGB). SHAP analysis revealed consistent key features across models. When use was restricted to black individuals, performance fell considerably.</p>
Conclusions: ML models based on routine blood tests effectively identify HbS and HbC carriers in a mixed general population. This approach has the potential to enhance screening efficiency by reducing reliance on specialised techniques.</p>
8 Authors
- Frederik Christensen
- Deniz Kenan Kılıç
- Alexander Djupnes Fuglkjær
- Jesper Petersen
- Tarec Christoffer El-Galaly
- Andreas Glenthøj
- Jens Helby
- Izabela Ewa Nielsen
1 Application
| Application ID | Title |
|---|---|
| 99692 | Risk of death, cause-specific death, hematologic cancer, non-hematologic cancer and other adverse outcomes in individuals with incidentally discovered paraclinical tests indicating hematologic disease |
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