BACKGROUND AND OBJECTIVES Diagnosis and early intervention of chronic kidney disease are essential to prevent loss of kidney function and a large amount of financial resources. To this end, we developed a fuzzy logic-based expert system for diagnosis and prediction of chronic kidney disease and evaluate its robustness against noisy data. METHODS At first, we identified the diagnostic parameters and risk factors through a literature review and a survey of 18 nephrologists. Depending on the features selected, a set of fuzzy rules for the prediction of chronic kidney disease was determined by reviewing the literature, guidelines and consulting with nephrologists. Fuzzy expert system was developed using MATLAB software and Mamdani Inference System. Finally, the fuzzy expert system was evaluated using data extracted from 216 randomly selected medical records of patients with and without chronic kidney disease. We added noisy data to our dataset and compare the performance of the system on original and noisy datasets. RESULTS We selected 16 parameters for the prediction of chronic kidney disease. The accuracy, sensitivity, and specificity of the final system were 92.13 %, 95.37 %, and 88.88 %, respectively. The area under the curve was 0.92 and the Kappa coefficient was 0.84, indicating a very high correlation between the system diagnosis and the final diagnosis recorded in the medical records. The performance of the system on noisy input variables indicated that in the worse scenario, the accuracy, sensitivity, and specificity of the system decreased only by 4.43 %, 7.48 %, and 5.41 %, respectively. CONCLUSION Considering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.

中文翻译：

---

预测慢性肾脏疾病的临床决策支持系统：模糊专家系统方法。

背景和目的慢性肾脏病的诊断和早期干预对于预防肾功能丧失和大量财务资源至关重要。为此，我们开发了一种基于模糊逻辑的专家系统，用于诊断和预测慢性肾脏病，并针对嘈杂的数据评估其鲁棒性。方法首先，我们通过文献综述和对18位肾脏病医生的调查，确定了诊断参数和危险因素。根据所选择的功能，通过回顾文献，指南并咨询肾脏病医生，确定了一组用于预测慢性肾脏疾病的模糊规则。使用MATLAB软件和Mamdani推理系统开发了模糊专家系统。最后，使用从216个随机选择的有或没有慢性肾脏病患者的病历中提取的数据对模糊专家系统进行了评估。我们向数据集中添加了噪声数据，并比较了原始数据集和噪声数据集上系统的性能。结果我们选择了16个参数来预测慢性肾脏疾病。最终系统的准确性，敏感性和特异性分别为92.13％，95.37％和88.88％。曲线下的面积为0.92，Kappa系数为0.84，表明系统诊断与病历中记录的最终诊断之间具有很高的相关性。系统在嘈杂的输入变量上的性能表明，在最坏的情况下，系统的准确性，灵敏度和特异性分别仅降低了4.43％，7.48％和5.41％。