Explainable Artificial Intelligence (XAI) is essential for ensuring transparency and trust in healthcare. However, a systematic evaluation of XAI methods for tabular healthcare data is lacking. In this paper, we present a comprehensive benchmarking framework designed to evaluate the quality of explanations produced by local and global explainable AI (XAI) methods applied to tabular healthcare data. The framework integrates predictive model-centered evaluation, which assesses how well explanations capture model behavior, and human-centered evaluation, which measures alignment between explanations and expert-defined clinical reasoning. We apply this framework to a stroke prediction dataset and evaluate it with six predictive models (i.e., interpretable models, tree-based ensemble algorithms, and kernel-based methods) representing a wide range from interpretable to black-box models. These models are explained using nine different local and global XAI methods covering both model-agnostic and model-specific techniques to provide diverse methodological coverage. The benchmark results provide practical insights into the strengths and limitations of different XAI methods across model types and explanation scopes, offering a structured guide for selecting reliable and clinically meaningful XAI method–predictive model combinations for healthcare decision support systems.