Predicting Hypertension Medication Uptake Using Explainable Artificial Intelligence: Evidence from a Kenyan Population-based Study

Abstract

Hypertension is a major contributor to cardiovascular morbidity and mortality worldwide, more so in Kenya, with limited progress towards achieving Africa's 2030 fast-track hypertension targets, especially in management. This study aimed to build a machine learning model to predict hypertension medication uptake in Kenya. Using data from 4,687 female and 5,269 male respondents from the 2022 Kenya Demographic and Health Survey, we applied Extreme Gradient Boosting, Support Vector Machine, Random Forest, and Elastic Net models. Data from 15 counties were split into training (80%) and testing (20%) sets, with class imbalance addressed using the Synthetic Minority Oversampling Technique and validation through leave-one-county-out cross-validation. The best-performing model, based on mean f1-score, was retrained using features selected through Sequential Forward Floating Selection. SHapley Additive exPlanations were used to interpret feature importance and directionality by sex. Treatment coverage remained suboptimal, with 26.6% of hypertensive males and 32.4% of females untreated. The XGBoost model achieved the best performance (78% males; 81% females). The most predictive features in both sexes were age, household size, sedentary time, income, exercise, wealth, residence duration, television viewership, and reproductive preferences among females. Interpretable machine learning revealed distinct sex-specific socio-behavioural predictors of hypertension treatment uptake in Kenya. Incorporating such data-driven insights can inform targeted, equitable interventions and strengthen hypertension control, especially in resource-limited settings where routine survey data can complement clinical assessments.