Leveraging Machine Learning and Big Data Analytics to Transform Service Delivery in Public Healthcare

Abstract

This study targets service-delivery improvements in South African public hospitals by pairing Big Data analytics with machine learning, implemented under a quantitative Cross-Industry Standard Process for Data Mining (CRISP-DM) workflow. We (i) identified determinants of effective Big Data analytics in public healthcare, (ii) compared analytical methods, (iii) built a predictive model of service outcomes, and (iv) assessed its performance and interpretability. Using a synthetic hospital dataset, we engineered features and trained a stacked ensemble (XGBoost, Random Forest, CatBoost, LightGBM) to classify outcomes as Improved, Unchanged, or Worsened. Baseline models (e.g., Support Vector Machine (SVM), logistic regression) were explored but not retained due to scalability and multiclass performance constraints. The final ensemble achieved 84% accuracy with class-wise Area Under the Curves (AUCs) of 0.93-0.95. Post-hoc SHapley Additive exPlanations (SHAP) analysis highlighted Satisfaction Rating, Age, and log (Wait Time) as the most influential features, and revealed interaction effects such as Wait-length interactions that align with operational intuition. To support practical use, we prototyped a Streamlit application that delivers near-real-time, explainable predictions for front-line decision support. The findings indicate that Machine Learning (ML)-driven Big Data analytics can provide scalable, interpretable insights to inform predictive decision-making, resource allocation, and patient-flow management in the public sector. While results are demonstrated on synthetic data, the approach establishes a deployable pathway for hospital analytics in South Africa and a foundation for validation on real-world datasets.

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