Machine Learning-Based Detection of HbS and HbC Carriers in the UK General Population.

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.

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

SUBMITTER: Christensen F 

PROVIDER: S-EPMC12584039 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature