IEEE Rev Biomed Eng. 2018;11:208-216. doi: 10.1109/RBME.2017.2787480. Epub 2017 Dec 25.
Individuals with chronic kidney disease (CKD) are often not aware that the medical tests they take for other purposes may contain useful information about CKD, and that this information is sometimes not used effectively to tackle the identification of the disease. Therefore, attributes of different medical tests are investigated to identify which attributes may contain useful information about CKD. A database with several attributes of healthy subjects and subjects with CKD are analyzed using different techniques. Common spatial pattern (CSP) filter and linear discriminant analysis are first used to identify the dominant attributes that could contribute in detecting CKD. Here, the CSP filter is applied to optimize a separation between CKD and nonCKD subjects. Then, classification methods are also used to identify the dominant attributes. These analyses suggest that hemoglobin, albumin, specific gravity, hypertension, and diabetes mellitus, together with serum creatinine, are the most important attributes in the early detection of CKD. Further, it suggests that in the absence of information on hypertension and diabetes mellitus, random blood glucose and blood pressure attributes may be used.
患有慢性肾病 (CKD) 的个体通常不知道他们为其他目的进行的医学检查可能包含有关 CKD 的有用信息，而且这些信息有时并没有被有效地用于识别该疾病。因此，研究了不同医学检查的属性，以确定哪些属性可能包含有关 CKD 的有用信息。使用不同的技术分析了具有多个属性的健康受试者和 CKD 受试者的数据库。首先使用共同空间模式 (CSP) 滤波器和线性判别分析来识别可能有助于检测 CKD 的主要属性。在这里，应用 CSP 滤波器来优化 CKD 和非 CKD 受试者之间的分离。然后，还使用分类方法来识别主要属性。这些分析表明，血红蛋白、白蛋白、比重、高血压和糖尿病，以及血清肌酐，是早期检测 CKD 的最重要属性。此外，还表明在缺乏高血压和糖尿病信息的情况下，可以使用随机血糖和血压属性。
