Predicting stroke in HIV patients using machine learning and electronic health records: a national and regional analysis

Stroke is a significant contributor to morbidity and mortality, particularly among people living with HIV (PLWH), who experience elevated incidence rates due to a higher prevalence of risk factors, such as hypertension, dyslipidemia, and chronic inflammation. The effective prediction and early identification of stroke in this population are critical but remain underexplored. This study employs machine learning (ML) algorithms, random forest and XGBoost, to predict stroke occurrence in PLWH using large-scale Electronic Health Record (EHR) datasets, including a national (U.S.) cohort and a regional (Tennessee) cohort. Both models demonstrated strong predictive performance, with random forest achieving an area under the receiver operating characteristic curve (AUROC) of 0.94 and XGBoost achieving 0.93 in the national dataset. The Tennessee cohort models exhibited lower yet notable predictive capability (AUROC ∼0.82), likely reflecting smaller sample size and cohort differences. In both cohorts, the two ensemble models outperformed a logistic regression, a feedforward deep neural network, and an adapted Framingham Stroke Risk Profile. Permutation-based feature-importance analyses identified clinical features associated with stroke risk. These findings suggest that ML models can effectively predict stroke among PLWH and highlight potential modifiable risk factors that could guide targeted preventive interventions.