Towards an Explainable Machine Learning System for Early Detection of Pediatric Sepsis in Low-Resource Hospital Settings in Nigeria: Challenges and Applications

Pediatric sepsis remains one of the most pressing threats to child survival in low- and middle-income countries, with Nigeria facing particularly high death rates due to delays in diagnosis and weak healthcare infrastructure. This paper reviews the potential of explainable machine learning (XAI) to improve early detection of sepsis in Nigeria's resource-limited hospitals. Unlike conventional black-box models, XAI offers transparency, providing clinicians with interpretable predictions that can bridge gaps created by nonspecific symptoms and limited diagnostic tools. However, several challenges persist, including unreliable manual records, frequent electricity shortages, biases in models trained on data from high-income countries, and limited trust among healthcare providers. Cultural perceptions, low AI literacy, and ethical concerns around data privacy further complicate adoption. Despite these obstacles, XAI offers practical opportunities such as real-time monitoring through mobile platforms and wearable devices, enabling earlier detection by both clinicians and community health workers. Methods like SHAP and LIME can build confidence by making predictions interpretable, while hybrid models that integrate local clinical guidelines with ML algorithms may enhance sensitivity. Drawing on successful pilots in other African contexts, this study proposes a framework for Nigeria that combines digital health innovations, workforce training, and infrastructure improvements to reduce diagnostic delays. With such strategies, XAI could significantly strengthen pediatric care, reduce uncertainties in diagnosis, and help close the healthcare gap between urban and rural populations.