Explainable AI (XAI) has become a hot topic

across multiple sectors. In practical applications, classification

models are severely constrained by the absence of

transparency, which undermines trust and has a black-box

nature, leading to a range of problems. Classification models

necessitate the use of XAI approaches to address these

limitations effectively. The Mean Evaluation of Metrics

Change (MEMC) is a novel metric introduced in this research

for evaluating the performance of Explainable AI techniques

on a global scale, like post-hoc and intrinsic XAI for

classification techniques on tabular data. The

proposed MEMC metric is formed from a combination of the

existing standard evaluation measures used for evaluating

classification. The proposed MEMC has proven to be the

convenient metric for determining the best explainer for a

produced classification. The proposed MEMC metric is

validated using a heart dataset from the healthcare sector. The

experimental results show that the Artificial Neural Network

(ANN) approach performed effectively on the heart dataset as

an intrinsic XAI in machine learning. Deep Neural Network

(DNN) also performs better as an intrinsic XAI technique

when applied to this dataset. Furthermore, ANCHORS has

shown strong performance as a post-hoc XAI technique when

Random Forest (RF) and XG-Boost are used as classification

models.