Kidney cancer is a dangerous disease affecting many patients all over the world. Early-stage diagnosis and correct identification of kidney cancer subtypes play an essential role in the patient's survival; therefore, its subtypes diagnosis and classification are the main challenges in kidney cancer treatment. Medical studies have proved that miRNA dysregulation can increase the risk of cancer. Thus, in this paper, we propose a new machine learning approach for significant miRNAs identification and kidney cancer subtype classification to design an automatic diagnostic tool. The proposed method contains two main steps: feature selection and classification. First, we apply the feature selection algorithm to choose the candidate miRNAs for each subtype. The feature selection algorithm utilizes the AMGM measure to select significant miRNAs with high discriminant power. Next, the candidate miRNAs are fed to a classifier to evaluate the candidate features. In the classification step, the proposed self-organizing deep neuro-fuzzy system is employed to classify kidney cancer subgroups. The new deep neuro-fuzzy system consists of a deep structure in the rule layer and novel architecture in the fuzzifier layer. The proposed self-organizing deep neuro-fuzzy system can help us to overcome the main obstacles in the field of neuro-fuzzy system applications, such as the curse of dimensionality. The goal of this paper is to illustrate that the neuro-fuzzy system can very useful in high dimensional data, such as genomics data, using the proposed deep neuro-fuzzy system. The obtained results illustrated that our proposed method has succeeded in classifying kidney cancer subtypes with high accuracy based on the selected miRNAs.

肾脏癌是一种危险的疾病，影响着世界各地的许多患者。肾癌亚型的早期诊断和正确识别在患者的生存中起着至关重要的作用。因此，其亚型的诊断和分类是肾癌治疗的主要挑战。医学研究证明，miRNA失调可增加患癌症的风险。因此，在本文中，我们提出了一种用于重大miRNA识别和肾癌亚型分类的新型机器学习方法，以设计一种自动诊断工具。所提出的方法包括两个主要步骤：特征选择和分类。首先，我们应用特征选择算法为每种亚型选择候选miRNA。特征选择算法利用AMGM度量来选择具有高判别力的重要miRNA。接下来，将候选miRNA送入分类器以评估候选特征。在分类步骤中，采用提出的自组织深层神经模糊系统对肾癌亚组进行分类。新的深层神经模糊系统由规则层的深层结构和模糊器层的新颖结构组成。提出的自组织深层神经模糊系统可以帮助我们克服神经模糊系统应用领域中的主要障碍，例如维数的诅咒。本文的目的是说明神经模糊系统在使用拟议的深度神经模糊系统的高维数据（例如基因组数据）中非常有用。