Intelligence-based predictive modeling of household health insurance coverage and associated socioeconomic predictors in Somalia

Introduction Achieving Universal Health Coverage in fragile settings remains difficult, and Somalia has one of the lowest coverage profiles in sub-Saharan Africa. Evidence on household insurance coverage and its socioeconomic predictors is limited, and conventional models may not capture the complex nonlinear patterns in survey data. The objective of this study was to apply machine learning algorithms to predict health insurance coverage and identify the most influential predictors associated with household health insurance coverage. Methods A cross-sectional analytical study was conducted in Somalia utilizing nationally representative demographic survey data. The analytic sample comprised 6,132 unweighted households, representing a nationally weighted sample of 7,933 households after applying survey sampling weights. Six supervised classification algorithms were compared: logistic regression, decision tree, random forest, gradient boosting, k-nearest neighbors, and support vector machine. The dataset was stratified and split into an 80% training set and a 20% hold-out test set. Hyperparameters and classification thresholds were optimized strictly using 5-fold stratified cross-validation on the training set. To manage extreme class imbalance, class weighting, sample weights, and Precision-Recall threshold tuning were applied. The model performance was evaluated on the independent hold-out test set using accuracy, precision, recall, F1-score, Precision-Recall Area Under the Curve (PR-AUC) and area under the receiver operating characteristic curve (ROC-AUC). Interpretability was achieved using permutation importance and SHapley Additive exPlanations (SHAP). Results The weighted prevalence of household health insurance coverage was 2.73%, with 217 insured and 7,716 uninsured households. Random forest showed the best overall discrimination, with a ROC-AUC of 87.91% (95% CI: 80.12–94.75), a PR-AUC of 33.56%, and an F1-score of 41.82%. It also achieved the highest recall (67.65%) for identifying insured households, with a precision of 30.26%. Gradient boosting performed similarly, with ROC-AUC of 87.76% and a PR-AUC of 32.75%. Across the top-performing models, region, household wealth, and residence were the most influential predictors, with additional contributions from household size, sex of the household head, and media exposure. SHAP analysis further indicated that wealth, region, and residence made substantial contributions to model predictions of insurance coverage. Conclusion Household insurance coverage in Somalia is extremely low and varies substantially by geography and socioeconomic position. Intelligence-based predictive modeling, particularly random forests and gradient boosting, provides an exploratory decision-support approach for understanding disparities and identifying geographic and socioeconomic patterns that may inform future health financing and resource-planning assessments, while the modest positive predictive value necessitates human verification before any operational use, though modest positive predictive value necessitates human verification to avoid resource misallocation.