Based upon the predictive ability and reliability of the models constructed by four machine learning approaches, XGB was considered as the most optimal diagnostic model for the AD, and the five most important variables including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1 were used to construct a five-gene diagnostic model for AD. This evidence concerns the gene CACNA2D3 and Alzheimer disease.