Among the numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies in general, and in different medical domains specifically. \nTo develop proposed model, with the aim of obtaining the best array of features, first and foremost, feature ranking techniques such as the Fisher’s discriminant ratio and the class separability criteria were used to prioritize features. Then, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features using high capabilities of this algorithm in the optimization process. Next, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. Finally, a fuzzy method was applied to integrate and finalize the decision making from among the recommended classes of the previous stage.\nThe evaluation process of the proposed model has been carried out on six datasets. Furthermore, in order to evaluate the efficacy of the proposed model, its performance was compared with thirteen well-known classification models. The experimental findings indicates that the new proposed hybrid model has a better performance compared to all thirteen classification methods based on all six used data sets, and has also significantly improved the classification accuracy.