Hamedan Farahnaz, Orooji Azam, Sanadgol Houshang, Sheikhtaheri Abbas
School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Islamic Republic of Iran.
School of Medicine, North Khorasan University of Medical Sciences (NKUMS), North Khorasan, Islamic Republic of Iran.
Int J Med Inform. 2020 Jun;138:104134. doi: 10.1016/j.ijmedinf.2020.104134. Epub 2020 Mar 30.
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.
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.
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.
Considering the desirable performance of the proposed expert system, the system can be useful in the prediction of chronic kidney disease.
慢性肾脏病的诊断和早期干预对于预防肾功能丧失和节省大量财政资源至关重要。为此，我们开发了一种基于模糊逻辑的慢性肾脏病诊断和预测专家系统，并评估其对噪声数据的鲁棒性。
首先，我们通过文献综述和对18位肾脏病专家的调查确定了诊断参数和风险因素。根据所选特征，通过查阅文献、指南并咨询肾脏病专家，确定了一组用于预测慢性肾脏病的模糊规则。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份随机选择的患有和未患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评估。我们向数据集中添加了噪声数据，并比较了系统在原始数据集和噪声数据集上的性能。
我们选择了16个参数用于预测慢性肾脏病。最终系统的准确率、灵敏度和特异度分别为92.13%、95.37%和88.88%。曲线下面积为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非常高的相关性。系统在噪声输入变量上的性能表明，在最坏的情况下，系统的准确率、灵敏度和特异度仅分别下降了4.43%、7.48%和5.41%。
考虑到所提出的专家系统具有理想的性能，该系统可用于慢性肾脏病的预测。
