A fairness-aware machine learning framework for maternal health in Ghana: integrating explainability, bias mitigation, and causal inference for ethical AI deployment

Antenatal care (ANC) uptake in Ghana remains inequitable, with socioeconomic and geographic disparities limiting progress toward universal maternal health coverage (SDG 3). We present a novel, fairness-aware machine learning framework for predicting antenatal care uptake among women in Ghana, integrating explainability, bias mitigation, and causal inference to support ethical artificial intelligence (AI) deployment in low- and middle-income countries. Using the 2022 Ghana Demographic and Health Survey (n = 3,314 eligible women with a recent live birth), we applied multiple imputation by chained equations (m = 10), appropriate categorical encoding, and synthetic minority oversampling (SMOTE) within training folds. Four supervised models (logistic regression, random forest, XGBoost, support vector machine) underwent stratified 5‑fold nested cross‑validation with cost‑sensitive threshold optimization (selected probability threshold = 0.45). Explainability (SHAP), fairness auditing (AIF360; metrics: statistical parity difference, disparate impact, equal opportunity difference, average odds difference, theil index), preprocessing mitigation (reweighing), counterfactual explanations (DiCE), and cautious treatment effect estimation (causal forests within a double machine learning framework) were integrated. Performance metrics included accuracy, precision, recall, F1, ROC‑AUC, minority class PR‑AUC, balanced accuracy, calibration (Brier score), and decision curve net benefit. The optimized random forest model achieved the highest accuracy (0.68) and recall (0.84) in identifying women with inadequate ANC contacts. Calibration was strong, with a brier score of 0.158, a calibration slope of 0.97, and an intercept of − 0.02. Fairness auditing revealed baseline disparities in model predictions across wealth, region, ethnicity, and religion, with a statistical parity difference for wealth status of 0.182 and a Disparate Impact of 1.62. Following reweighting, disparate impact improved into the fairness range (0.92; within the recommended 0.8–1.25 interval), and statistical parity difference reduced to − 0.028. Counterfactual analysis indicated that education, wealth, media exposure, and health worker contacts were the most modifiable factors for improving ANC uptake. Exploratory causal inference using double machine learning suggested that improving wealth status and education could be associated with a 16% (Average Treatment Effect [ATE] = 0.163) and 14% (ATE = 0.142) increase, respectively, in the probability of adequate ANC, with greater effects observed among urban and educated subgroups. Adjusted odds ratio (AOR) analysis showed that women in the richest quintile were nearly twice as likely to receive adequate ANC (AOR = 1.91, 95% CI: 1.44–2.53; p < 0.001), while those in the poorest quintile had significantly lower odds (AOR = 0.58, 95% CI: 0.45–0.75; p < 0.001). Additional significant predictors included health insurance coverage (AOR = 1.74, 95% CI: 1.19–2.55), health worker contacts (AOR = 1.33, 95% CI: 1.11–1.58), and pregnancy intention (AOR = 1.54, 95% CI: 1.30–1.82). This integrated, fairness-aware machine learning framework suggest robust, equitable, and actionable prediction of ANC uptake among Ghanaian women. Key modifiable determinants include wealth, education, and healthcare access barriers. The framework offers a replicable, ethical blueprint for transparent and fair AI deployment in maternal health, supporting targeted interventions to advance universal access to quality care in Ghana. Policymakers and health managers can leverage these AI tools to identify high-risk women, monitor intervention impacts, and allocate resources more equitably, advancing progress toward universal access to quality maternal care in Ghana.