Information Technology Reference
In-Depth Information
TP
sensitivit
y
=
(%)
(8)
TP
+
FN
TN
specificit
y
=
(%)
(9)
FP
+
TN
where TP, TN, FP and FN denote true positives, true negatives, false positives, and
false negatives, respectively.
True positive (TP)
: An input is detected as a patient with atherosclerosis diagnosed by
the expert clinicians.
True negative (TN)
: An input is detected as normal that is labeled as a healthy person
by the expert clinicians.
False positive (FP)
: An input is detected as a patient that is labeled as a healthy by the
expert clinicians.
False Negative (FN)
: An input is detected as normal with atherosclerosis diagnosed
by the expert clinicians.
4.1.3 k-Fold Cross-Validation
K-fold cross validation is one way to improve the holdout method. The data set is
divided into
k
subsets, and the holdout method is repeated
k
times. Each time, one of
the
k
subsets is used as the test set and the other
k-1
subsets are put together to form a
training set. Then the average error across all
k
trials is computed. The advantage of
this method is that it is not important how the data is divided. Every data point ap-
pears in a test set exactly once, and appears in a training set
k-1
times. The variance of
the resulting estimate is reduced as
k
is increased. The disadvantage of this method is
that the training algorithm must be rerun from scratch
k
times, which means it takes
k
times as much computation to make an evaluation. A variant of this method is to
randomly divide the data into a test and training set
k
different times. The advantage
of this method is that we can independently choose the size of the each test and the
number of trials [14].
4.2 Results and Discussion
Fuzzy-resource allocation mechanism provided Fuzzy-AIRS to classify Atherosclero-
sis disease with 100% classification accuracy using 10-fold cross validation.
The relation between resource number and classification accuracy in Fuzzy-AIRS
and AIRS for the diagnosis of Atherosclerosis disease is shown in Table 3 and 4. Also
they present the obtained classification accuracy and sensitivity and specifity values
of AIRS and Fuzzy-AIRS classifier algorithms. As can be seen in Table 3 and Table 4,
AIRS with fuzzy resource allocation mechanism is very effective classifier more than
original AIRS. This improvement in performance is also very important especially in
medical field and in applications that use large datasets.
