hepatitis B carriers, the third represents healthy non-carriers, and the fourth is unused. For each
input pattern, the first firing effector neuron is recorded. The initial effector neuron group firing
was defined as the output associated with an input pattern. When the initial effector neuron-firing
group is the same as the group determined by a physician, the system makes a correct response.
The greater the number of correct responses made by the system, the higher its fitness. The input –
output interface of the ANM system with the clinical hepatitis B database is shown in Fig. 8.
4. Experimental results
Four types of experiments were performed with the system. The first distinguished the chronic
hepatitis B patients from healthy individuals in the two-category set, based on the ten physical
examination parameters. To understand more about the differentiation capability, the system
was also trained to differentiate chronic hepatitis B patients, hepatitis B carriers, and healthy non-
carriers in the three-category set. The second ex- periment tested the diagnosis capability of the
system. The third experiment examined the effec- tiveness of each parameter in identifying if a
patient was infected with chronic hepatitis B. The fourth experiment investigated the effectiveness of
considering two examination parameters simulta- neously in determining if a patient was infected
with chronic hepatitis B.
4
.
1
. Differentiation capability The experiments with the two-category set
show that the number of patterns recognized by the system increased significantly in the early
learning stage and slowed down in the later stage Fig. 9. For example, after 16 learning cycles, the
system was able to differentiate 368 out of 676 patterns. The differentiation rate was 54.4 368
676. It took 251 cycles for the system to learn to recognize 596 training patterns 88.2. After
4259 cycles, the system was able to differentiate 670 patterns correctly 99.1. The system recog-
nized no additional patterns until cycle 10 256, when it was able to differentiate 671 patterns
99.3. We note that it seemed to be very ineffi- cient for the system to take nearly 8000 cycles to
increase the number of recognized patterns from
Fig. 8. Interface of the ANM system with the hepatitis B database. Each patient’s data, consisting of ten physical examination items, are transformed into a 42-bit pattern. When the group of the first firing effector neuron is the same as the group determined by
physician, the system makes a correct response.
Fig. 9. Learning curves of the ANM system.
patterns recognized from 341 to 342. This is con- sistent with our previous experimental result
Chen and Conrad, 1997b that long periods of stasis are punctuated by periods of rapid evolu-
tionary learning, implying that subtle evolution- ary
changes at
one level
may open
up opportunities
for more
visible evolutionary
changes at another level.
4
.
2
. Diagnosis capability As noted earlier, the clinical hepatitis B data-
base comprised 1051 records, divided into two sets: a two-category set and a three-category set.
The two-category set and the three-category set were independent in the sense that none of the
records were repeated in these two data sets. There were no major differences regarding the
ten examination items in these two sets, except the number of categories determined by a physi-
cian hepatitis B carriers comprised an additional category in the three-category set. Here, the sys-
tem was tested after substantial learning with the two-category set. Two test sets generated from
the three-category set were used. Combining the hepatitis B carrier and chronic hepatitis B patient
categories together to be referred to as case 1 generated the first set. Combining the hepatitis B
carrier and healthy non-carrier categories to- gether to be referred to as case 2 produced the
second set. If the system can recognize the ma- jority of the patterns in case 1, we can conclude
that there is no difference between a hepatitis B carrier and a chronic hepatitis B patient. Simi-
larly, if the system can achieve a high differentia- tion rate in case 2, this means that there is no
difference between a hepatitis B carrier and a healthy non-carrier.
Experimental results demonstrated that, in case 1, the system was able to differentiate 236
out of the 375 records. The differentiation rate was 62.9 236375. In case 2, the system was
able to differentiate 369 out of the 375 records. The differentiation rate was 95.7 359375.
This result implies that, from the viewpoint of physicians, a hepatitis B carrier is more like a
healthy non-carrier than like a chronic hepatitis B patient.
670 to 671. However, this illustrates that the system has long-term learning capability, which
allows it to continue to improve. The second part of the experiment differenti-
ated 375 patterns in the three-category set. This set was equally divided into three groups:
chronic hepatitis B patients, hepatitis B carriers, and healthy non-carriers. After the first learning
cycle, the system was able to differentiate 95 out of 375 patterns 25.3. After cycle 6138, it was
able to recognize 339 training patterns 90.4. Then, learning slowed down significantly. It took
another 6235 cycles for the system to recognize only one more pattern i.e. to increase the num-
ber of patterns recognized from 339 to 340. We note that, after cycle 16 182, the system was able
to differentiate 341 patterns. Only another 144 cycles were required to increase the number of
4
.
3
. Parameter analysis In the following experiment, we investigated the
effectiveness of each parameter in determining whether an individual was infected with chronic
hepatitis B. We used the system after substantial learning with the two-category set and tested it
with the variants of the two-category set, instead of the three-category set. There were 42 test sets in
total, corresponding to 42 bits used to represent the ten examination items. We note that one of
these 42 sets was generated for sex, one for HcAb, and five each for the other eight parameters. The
first test set was exactly the same as the two-cate- gory set except for the first bit of each pattern
representing sex. A modification was made by changing all of the 0s to 1s and 1s to 0s for the first
bit of the patterns in the two-category set. The second test set was generated in a similar manner,
but the modification was made at the second bit position representing HcAb. We note that the
system learned to treat a particular bit position as rather significant when it can always be used to
give a correct response to all the training patterns. In other words, altering a significant bit might lead
to a complete failure. In contrast, the system tends to ignore positions whose bit values are not signifi-
cant. At an insignificant position any alteration of bit values has no effect on the response. Thus, we
may conclude that a parameter is insignificant when the system has the same outputs for both its
training and test sets, and significant if the results are quite different.
Unlike the above two sets, the remaining 40 test sets were generated in a different manner. As
indicated in the above, five test sets were generated for each of the other eight parameters not includ-
ing sex and HcAb. First, only the patterns associ- ated with chronic hepatitis B in the two-category
set were selected for testing. Secondly, each test set was varied by setting one of the eight parameters
at a specific value the other parameters were kept constant. The generation of each test set was
implemented by copying all of the patterns corre- sponding to the chronic hepatitis B patients in the
two-category set and then replacing five specific bit positions of each pattern with one of the five-bit
patterns shown in Eq. 1. For example, the pat- terns of the first set were identical to those of the
chronic hepatitis B patients in the two-category set, but the first five-bit positions of each pattern
were set at ‘00001’. The second test set was gener- ated in a similar manner, but the first five-bit
positions were set at ‘00010’.
The experimental results showed that the system is able to differentiate 99.5 of the patterns in the
first test set. That is, the results were the same when the sex of these clinical records was changed
to male if it was female and to female if male. This strongly suggests that there is essentially no rela-
tionship between sex and chronic hepatitis B, indicating that the chances of being infected with
chronic hepatitis B are basically the same for both males and females. In contrast, in the second test
set, the system was only able to recognize 342 out of the 671 patterns. The differentiation rate was
45.8 342671. This means that the result is nearly reversed when the HcAb of each record is
altered, illustrating that HcAb plays a significant role in determining whether one is infected with
chronic hepatitis B.
As to the other eight parameters, only GGT was not significant. Fig. 10 shows that, for any GGT
values, the chance of having chronic hepatitis B is close to zero, suggesting that there is no correla-
tion between GGT and chronic hepatitis B. By contrast, some correlation is revealed between TB
and chronic hepatitis B, even though this is not very obvious. Fig. 10 shows that most people
having low TB are not infected with chronic hepatitis B, and that 11 of the people having
Fig. 10. Effectiveness of each examination item in determining the probability of being infected with chronic hepatitis B.
high TB are infected. On the contrary, most peo- ple having comparatively high GOT are not in-
fected with chronic hepatitis B, and 7.7 of the people with low GOT are infected. It also shows
that the chance of having chronic hepatitis B is close to zero for people of different ages unless
they are very old. That is, age is not a significant factor for susceptibility to chronic hepatitis B, but
it becomes comparatively important when one is elderly. This suggests that the probability of being
infected with chronic hepatitis B does not neces- sarily increase as age increases.
The probability that one is infected with chronic hepatitis B is quite small when one has compara-
tively high Alb or Glo values, but it increases if either of these values goes down. In particular, the
probability goes up to 47.5 when one has very low Alb. The last two parameters are GPT and
ALP. The probability is pretty low when one has moderate GPT and ALP values, but becomes
higher if any of these values either increases or decreases.
The summary of the above results is that all parameters except for sex and GGT play some
role in determining whether one is infected with chronic hepatitis B. However, none of these
parameters is able to 100 positively determine whether one is infected with chronic hepatitis B,
suggesting that these indicators are multi-factorial. This prompted us to perform a further experiment
to be described in the Section 4.4 that considers two parameters together in determining the proba-
bility of being infected with chronic hepatitis B.
4
.
4
. GPTGOT and AlbGlo analysis As indicated in Section 3, both AlbGlo and
GPTGOT rates are also significant parameters frequently
used by
physicians to
determine whether one is infected with chronic hepatitis B.
Clinical reports suggest that those who are in- fected with chronic hepatitis B usually have very
low Alb. As to Glo, it might either increase slightly or have no significant changes at all. This
illustrates that the chance of being infected with chronic hepatitis B becomes higher if one’s Alb
Glo rate goes down. Generally speaking, the Alb Glo rate of a healthy individual ranges from 1.5 to
2.5. When this rate goes down significantly in particular for a decrease to less than 1, one might
be infected with chronic hepatitis, cirrhosis, or liver cancer.
We first investigated the effectiveness of differ- ent combinations of Alb and Glo in determining
whether one was infected with chronic hepatitis B. The system was tested after substantial learning
with the two-category set. As mentioned earlier, the Alb and Glo values were divided into five
levels, respectively i.e. low, moderately low, mod- erate, moderately high, and high. In total, there
were 25 possible combinations of Alb and Glo, with a test set generated for each combination.
The following explains how the test sets were generated. We note that only the patterns associ-
ated with healthy people in the two-category set were selected for testing. For each of these pat-
terns, the bits corresponding to Alb and Glo were set at specific levels, respectively. Setting both Alb
and Glo at a low level generated the first set. Setting Alb at a low level and Glo at a moderately
low level generated the second set. The remaining 23 test sets were generated in a similar manner.
The experimental results Fig. 11 showed that one has a 64 chance of being infected with
chronic hepatitis B if Alb is low and Glo is moderately high. Moreover, the probability goes
up to 72 when one has low Alb and high Glo. Both results were higher than those in the previous
experiment we note that the highest probability is 47.5 and 23.4, respectively, if we consider only
Alb and Glo alone. The results given above suggest that the probability of being infected with
chronic hepatitis B increases as the AlbGlo rate goes down.
We also investigated different combinations of GOT and GPT in determining the probability of
being infected with chronic hepatitis B. As noted earlier, the GPTGOT rate for a healthy individ-
ual is close to 1. This implies that one will have liver damage when his GPTGOT rate is either
greater than 1 or far less than 1. As above, the GPT and GOT values were divided into five
levels, respectively i.e. low, moderately low, mod- erate, moderately high, and high. In total, there
were 25 test sets, where all the possible combina- tions of GPT and GOT are considered. The result
Fig. 11. Effectiveness of the combinations of Alb and Glo in determining the probability of being infected with chronic hepatitis B.
showed that, regardless of GOT, it is unlikely that one will be infected with chronic hepatitis B when
his GPT is moderate i.e. neither too high nor too low. However, the probability becomes higher
when one’s GPT value increases or decreases sig- nificantly Fig. 12.
5. Conclusions
