Harnessing machine learning with auditory tests and demographic factors to forecast children’s reading abilities in children living with and without HIV

Abstract

Early identification of children at risk for reading difficulties is crucial to address HIV-related educational deficits and prevent long-term challenges, especially in populations affected by factors that impair cognitive development. Central auditory tests (CATs), which assess how the brain processes complex sounds, are strongly linked to reading ability and may help predict literacy outcomes. This study used four preprocessing methods to adjust for age-related variation in CAT scores, then applied machine learning to predict oral reading fluency in a longitudinal cohort of 251 children living with HIV (mean age 4.3 years − 50% female) and without HIV (mean age 4.8 years − 55% female). Early CAT performance combined with demographic factors (education quality, gender, HIV status) predicted later reading ability effectively. While predictions were stronger in the full sample than in HIV subgroups, this approach shows promise and lays the foundation for early literacy screening.