Leveraging Electronic Syndromic Surveillance Synthetic Data to Predict Diarrhoea in Zimbabwean Children Under-Five: An Explainable AI Framework

Abstract

This study investigates the use of synthetic data in developing an electronic syndromic surveillance system to predict diarrhoeal outcomes among Zimbabwean children under five. Given the high morbidity and mortality rates linked to diarrhoeal diseases in Zimbabwe, implementing a real-time surveillance system can significantly enhance early outbreak detection and response. Machine learning models, including Random Forest, XGBoost, and Long Short-Term Memory (LSTM), were trained on synthetic data created through Generative Adversarial Networks (GANs), simulating real-world conditions and enhancing dataset diversity. Explainable AI technique like SHAP was employed for model interpretability, revealing crucial predictors, such as healthcare-seeking behaviours and socio-demographic factors, which drive diarrheal outcomes. The findings emphasise the potential of AI-driven surveillance in low-resource settings, offering actionable insights for public health interventions. This research provides a foundational framework for implementing electronic syndromic surveillance to improve public health resilience in Zimbabwe.

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