Objective: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. Methods: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes. Results: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. Conclusions: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans.

中文翻译：

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用于识别慢性肾脏病 (CKD) 属性的临床适用机器学习方法，用于低成本诊断筛查

目的：慢性肾病（CKD）是全球主要的公共卫生问题。晚期诊断的高成本和检测设施不足会导致 CKD 患者的高发病率和死亡率，特别是在欠发达国家。因此，通过使用负担得起的计算机辅助诊断的重要参数分析进行的早期诊断不仅可以降低诊断成本，还可以改善患者管理和结果。方法：在这项研究中，我们开发了机器学习模型，使用选择性的关键病理类别来识别有助于准确早期诊断 CKD 的临床测试属性。这种方法将节省诊断筛查的时间和成本。我们还评估了几个分类器的性能，这些分类器在使用这些选定的临床测试属性得出的优化数据集上进行了 k 折交叉验证。结果：我们的结果表明，使用我们提出的机器学习模型，具有重要属性的优化数据集在 CKD 诊断中表现良好。此外，我们评估了基于尿液和血液测试的临床测试属性以及获得成本低的临床参数。具有优化和病理分类属性集的预测模型产生了高水平的 CKD 诊断准确性，其中随机森林 (RF) 分类器表现最佳。结论：