Abstract:
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 non-
CKD 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.
