Utility of a Machine-Guided Tool for Assessing Risk Behaviour Associated with Contracting HIV in Three Sites in South Africa: A field evaluation (Preprint)

BACKGROUND Digital data collection and the associated mobile health technologies have allowed for the recent exploration of machine learning (ML) as a tool for combatting the HIV epidemic. Machine learning has been found to be useful both in HIV risk prediction and as a decision support tool for guiding pre-exposure prophylaxis (PrEP) treatment. OBJECTIVE This paper reports data from two sequential studies evaluating the viability of using machine learning to predict the susceptibility of adults to HIV infection using responses from a digital survey deployed in a high burden, low-resource setting. METHODS 1036 and 593 participants were recruited across two trials. The first trial was a cross-sectional study in one location and the second trial was a cohort study across three trial sites. The data from the studies were merged, partitioned using standard techniques, and then used to train and evaluate multiple different machine learning models and select and evaluate a final model. Variable importance estimates were calculated using the PIMP and SHAP methodologies. RESULTS Characteristics associated with HIV were consistent across both studies. Overall, HIV positive patients had a higher median age (34 [IQR: 29-39] vs 26 [IQR 22-33], p CONCLUSIONS This study has highlighted the synergies present between mobile health and ML in HIV. It has been demonstrated that a viable ML model can be built using digital survey data from an low-middle income setting with potential utility in directing health resources. CLINICALTRIAL This study is registered on the South African National Clinical Trial Registry (www.sanctr.gov.za; DOH-27-042021-679).