Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases

Abstract

Meningitis, characterized by meninges and cerebrospinal fluid inflammation, poses diagnostic challenges due to diverse clinical manifestations. This work introduces an explainable AI automatic medical decision methodology that determines critical features and their relevant values for the differential diagnosis of various meningitis cases. We proceed with knowledge acquisition to define the rules for this research. Currently, we have established the etiological diagnosis of Meningococcaemia, Meningococcal Meningitis, Tuberculous Meningitis, Aseptic Meningitis, Haemophilus influenzae Meningitis, and Pneumococcal Meningitis. The data preprocessing was conducted after collecting data from samples with meningitis diseases at Setif Hospital in Algeria. Tree-based ensemble methods were then applied to assess the model’s performance. Finally, we implement an XAI agnostic explainability approach based on the SHapley Additive exPlanations technique to attribute each feature’s contribution to the model’s output. Experiments were conducted on the collected dataset and the SINAN database, obtained from the Brazilian Government’s Health Information System on Notifiable Diseases, which comprises 6729 patients aged over 18 years. The Extreme Gradient Boosting model was chosen for its superior performance metrics (Accuracy: 0.90, AUROC: 0.94, and F1-score: 0.98). Setif’s hospital data revealed notable performance metrics (Accuracy: 0.7143, F1-Score: 0.7857). This study’s findings showcase each feature’s contribution to the model’s predictions and diagnosis. It also reveals critical biomarker ranges associated with distinct types of Meningitis. Significant diagnostic effect was found for Meningococcal Meningitis with elevated neutrophil levels ( $ \gt $ 40%) and balanced lymphocyte levels (40%–60%). Tuberculous Meningitis demonstrated low neutrophil levels ( $ \lt $ 60%) and elevated lymphocyte levels ( $ \gt $ 60%). H. influenzae meningitis exhibited a predominance of neutrophils ( $ \gt $ 80%), while Aseptic meningitis showed lower neutrophil levels ( $ \lt $ 40%) and lymphocyte levels within the range of 50%–60%. The majority of the AI automatic medical decision results are twinned with validation by our team of infectious disease experts, confirming the alignment of algorithmic diagnoses with clinical practices.

Keywords

• explainability
• infection disease diagnosis
• meningitis disease
• SHapley Additive exPlanations
• XGBoost