The real industrial problems are often influenced by many uncertain factors, leading to computing issues by AI solutions, such as using medical data to predict cancer. As a new field, explainable AI still faces many challenges, such as the high-dimension of features to ex-plain. This paper proposes two possible solutions to the high-dimension XAI problem. The first solution is the Bi-level approach based on the method from cooperative coalitional game theory. In order to solve the high-dimension problem, the features are clustered using the historical input data set. In particular, the euclidean metric and size-constrained k-means algorithm are used. The second approach is based on the sampling algorithm for calculating the Shapley value, which makes it possible to compute the approximation of the Shapley value without using the coalitional game theory and clustering approaches. This paper implements the XAI solution in cancer prediction algorithm based on a Sampling approach and Bi-level approach, which is new in XAI field.