In the classifi cation of cancer data sets, we note that they contain a number of additional features that infl uence the classifi cation accuracy. There are many evolutionary algorithms that are used to defi ne the feature and reduce dimensional patterns such as the gray wolf algorithm (GWO) after converting it from a continuous space to a discrete space. In this paper, a method of feature selection was proposed through two consecutive stages in the fi rst stage, the fuzzy mutual information (FMI) technique is used to determine the most important feature selection of diseases dataset through a fuzzy model that was built based on the data size. In the second stage, the binary gray wolf optimization (BGWO) algorithm is used to determine a specifi c number of features affecting the process of classifi cation, which came from the fi rst stage. The proposed algorithm, FMI_BGWO, describes effi ciency and effectiveness by obtaining a higher classifi cation accuracy and a small number of selected genes compared to other competitor algorithms.How to cite this article: Noori NM, Qasim OS. Improving cancer diseases classifi cation using a hybrid fi lter and wrapper feature subset selection. Ann Proteom Bioinform. 2020; 4: 006-0011.