Genetic datasets have a large number of features that may signifi cantly affect the disease classifi cation process, especially datasets related to cancer diseases. Evolutionary algorithms (EA) are used to fi nd the fastest and best way to perform these calculations, such as the bat algorithm (BA) by reducing the dimensions of the search area after changing it from continuous to discrete. In this paper, a method of gene selection was proposed two sequent stages: in the fi rst stage, the fuzzy mutual information (FMI) method is used to choose the most important genes selected through a fuzzy model that was built based on the dataset size. In the second stage, the BBA is used to reduce and determine a fi xed number of genes affecting the process of classifi cation, which came from the fi rst stage. The proposed algorithm, FMI_BBA, describes effi ciency, by obtaining a higher classifi cation accuracy and a few numbers of selected genes compared to other algorithms.How to cite this article: Yonis AL-Taie FA, Qasim OS. Classifi cation of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm. Ann Proteom Bioinform. 2020; 4: 001-005.