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Machine Learning-Based Detection of HbS and HbC Carriers in the UK General Population
Frederik Christensen1, Deniz Kenan Kılıç2, Alexander Djupnes Fuglkjær1
1Artificial Intelligence for Operations Research Group Department of Materials and Production Aalborg University Aalborg Denmark.
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.
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.
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.
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.
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.
