Explainable AI modelling of Comorbidity in Pregnant Women and Children with Tropical Febrile Conditions

Abstract

Febrile diseases often exhibit overlapping symptoms, posing a challenge for their differential diagnosis. This challenge is particularly critical in pregnant women and children, where early and accurate diagnosis is vital to mitigate the elevated risk of maternal mortality prevalent in tropical and subtropical regions. Despite the commonality of fever as a symptom, the diverse range of potential co-morbidities necessitates an exploration of associated illnesses. This study employs the eXtreme Gradient Boosting (XGBoost) machine learning algorithm to classify febrile diseases' co-morbidities in pregnant women and children under 5 years. The dataset, comprising 1,350 records from selected health facilities across Niger-delta states in Nigeria, contributes to informed decision-making by physicians, ultimately enhancing healthcare provision. Evaluation results demonstrate the classifier's high precision (0.995) and recall (1.00) for the children dataset, while precision and recall of 1.00 are achieved for the pregnant women dataset. To facilitate model explanation and result interpretation, an eXplainable Artificial Intelligence (XAI) approach, specifically the SHapley Additive exPlanations (SHAP) method, is applied. The summary plot highlights upper and lower respiratory tract infections and malaria as the predominant diseases co-morbidities in children. In contrast, pregnant women exhibit upper and lower urinary tract infections, and malaria as the highest-ranking diseases co-morbidity. These results underscore the potential of ML techniques in accurately classifying febrile conditions' co-morbidities, contributing to the reduction of adverse health outcomes. The study's findings offer valuable insights for healthcare providers, enabling them to deliver more targeted and effective care to these vulnerable populations, thereby enhancing overall well-being.