Predicting Non-successful Ageing in Rural South Africa: A Prospective Cohort Study using Machine-Learning Analysis of the HAALSI Cohort

Later-life risk in rural South Africa unfolds under conditions of multimorbidity, socioeconomic inequality and uneven access to care, making it difficult to identify who is most vulnerable to adverse ageing trajectories. Using three waves of Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community (HAALSI) data and a cleaned survivor subset of 1,435 adults, we developed an interpretable machine-learning framework to predict non-successful ageing, defined by functional limitation or psychological distress at Wave 3. In repeated 10-fold cross-validation (5 repeats), discrimination increased from 0.774 ± 0.038 in a baseline model to 0.846 ± 0.031 with longitudinal change scores and 0.860 ± 0.029 in the full proximal model; a constrained artificial neural network (ANN) benchmark achieved 0.841 ± 0.042. SHapley Additive exPlanations identified baseline household assets and BMI change as the strongest contributors to prediction, with age and Wave 2 depressive symptoms contributing secondarily. Although rank discrimination was strong, calibration slope and intercept (3.661 and − 1.242) indicated compressed predicted probabilities. These findings suggest that later-life risk in rural South Africa is better captured by multidomain longitudinal information than by baseline characteristics alone and that community health screenings should prioritize monitoring household asset stability and BMI trends as early warning indicators of declining functional health, and that interpretable machine learning may support relative risk stratification, although absolute risk estimation and generalisability remain limited.