W. Kulei

Explainable AI for Malaria Diagnosis: Comparative Analysis of ML Models Using Random Forest Feature Selection and SHAP Interpretability

This framework illustrates, without any claim of clinical validity, how a leakage-safe ML pipeline and SHAP interpretability can be combined and rigorously self-audited; real patient-level data and external validation are required before any clinical inference is drawn.