  In this work we study numerical construction of optimal clinical diagnostic
tests for detecting sporadic Creutzfeldt-Jakob disease (sCJD). A cerebrospinal
fluid sample (CSF) from a suspected sCJD patient is subjected to a process
which initiates the aggregation of a protein present only in cases of sCJD.
This aggregation is indirectly observed in real-time at regular intervals, so
that a longitudinal set of data is constructed that is then analysed for
evidence of this aggregation. The best existing test is based solely on the
final value of this set of data, which is compared against a threshold to
conclude whether or not aggregation, and thus sCJD, is present. This test
criterion was decided upon by analysing data from a total of 108 sCJD and
non-sCJD samples, but this was done subjectively and there is no supporting
mathematical analysis declaring this criterion to be exploiting the available
data optimally. This paper addresses this deficiency, seeking to validate or
improve the test primarily via support vector machine (SVM) classification.
Besides this, we address a number of additional issues such as i) early
stopping of the measurement process, ii) the possibility of detecting the
particular type of sCJD and iii) the incorporation of additional patient data
such as age, sex, disease duration and timing of CSF sampling into the
construction of the test.
