Various methods have been proposed to diagnose renal failure based on data mining and artificial intelligence techniques. The use of data mining approaches, renal failure could be predicted based on several features and risk factors that exacerbate the condition. At the same time, each is associated with issues such as complex computation and a long implementation period. Moreover, these methods have varied accuracies due to their dependency on algorithms, performance, and nature of data. The present study aimed to propose an approach to increasing the accuracy and efficiency of the renal failure diagnosis. To this end, we developed a feature selection method based on the dragonfly algorithm. In addition, the optimal parameters of the support vector machine algorithm were presented using the preceding algorithm to optimize data classification. The performance of the proposed algorithm has been evaluated in comparison with the latest available methods. According to other algorithms, the proposed method is improved by 3.37% and 9.17% accuracy. Accordingly, in terms of accuracy compared to the latest work done, the proposed method has a significant improvement of 34.12%. The proposed method has been tested again on the information received from 7 patients from one of the specialized dialysis clinics in Mashhad.

已经提出了基于数据挖掘和人工智能技术来诊断肾衰竭的各种方法。使用数据挖掘方法，可以根据加重病情的几个特征和风险因素来预测肾功能衰竭。同时，每一个都与复杂的计算和较长的实施周期等问题相关。此外，这些方法由于依赖于算法、性能和数据性质而具有不同的准确性。本研究旨在提出一种提高肾功能衰竭诊断准确性和效率的方法。为此，我们开发了一种基于蜻蜓算法的特征选择方法。此外，利用上述算法给出了支持向量机算法的最优参数，以优化数据分类。与最新的可用方法相比，已经评估了所提出算法的性能。根据其他算法，所提出的方法分别提高了 3.37% 和 9.17% 的准确率。因此，与最新所做的工作相比，在准确性方面，所提出的方法显着提高了 34.12%。所提出的方法已根据来自马什哈德一家专门透析诊所的 7 名患者的信息再次进行了测试。