Automatic medical decision for diagnosis of infectious diseases basedon artificial intelligence approaches

Abstract

infectious totheoverlappingclini- Meningitis,inparticular,remainsasig-especiallywhen whichcanbetime-consumingand resource-intensive. ThisPh.D.researchinvestigatestheintegrationofartificialintelli- gence (AI) into the diagnostic process, aiming to enhance accuracy, speed, and inter- pretability through the use of explainable AI (XAI) techniques. The first phase of this study examines cerebrospinal fluid (CSF) biomarker vari- ations across different age groups—children, adults, and the elderly—within various types of meningitis.By analyzing these patterns, we aim to improve the understand- This analysis establishes a foundational understanding of how biomarkers behave in different populations and infection contexts. Ournextcontributionfocusesondiagnosingmultiplemeningitistypesusingensem- ble models and SHapley Additive exPlanations (SHAP) to interpret feature importance. Using data from Setif Hospital (Algeria) and Brazil’s SINAN database, we validated our findings across diverse populations.Extreme Gradient Boosting achieved strong performance(accuracy: 0.90,AUROC:0.94,F1-score: 0.98). SHAPrevealeddistinct biomarker profiles such as elevated neutrophils in meningococcal, high lymphocytes alongwithclin- guish bacterial, viral, and pathogen-specific meningitis, increasing trust in AI-driven diagnostics.