Kolmogorov Arnold Network for Multiple Disease Classification: Interpretability and Faithfulness Evaluation

Using five clinical datasets, this study evaluates KANs for interpretable medical classification. It compares KAN with established ML models and evaluates its intrinsic spline-based explanations against SHAP, permutation importance, and Random Forest feature importance. A deletion-based faithfulness analysis further verifies whether the identified important features genuinely influence model predictions.