The Learning Process

In the hybrid NN-CBR approach, learning encompasses three main tasks^; (1) training the neural networks;

(2) incorporating new cases in the case library;

(3) building the diagnosis descriptors.

The neural network is initially trained according to the learning algorithms detailed in the section 5.5. The cases used to train the network are also stored in the library in a sequential manner, as described in section 5.4. Then, the connections of the neural network are analysed and interpreted in diagnosis descriptors, in order to provide the system with symbolic representations of the diagnostic knowledge stored in the network.

We present here an example of the training of the neural network in the domain of CHD, as well as the construction of diagnosis descriptors for the diagnoses considered. Figure 5.11 shows an excerpt of a neural network computing punishments and rewards for the diagnoses of AVSD and VSD when case AVSD7 is presented.

^One more step could be included here, which is the knowledge-acquisition phase necessary for the construction of the domain knowledge hierarchy. Learning this type of declarative knowledge involves a knowledge engineering process where experts have to be interviewed and textbooks consulted. In Hull & King (1986) and Mattos (1991) there are more detailed explanations about the acquisition and representation of declarative knowledge using the abstraction concepts.

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The excerpts selected show the influence of four different findings in the identification of the two diagnoses.

Case presented: AVSD7

Left anterior henni biockoge

Down's syndrome

Increased pulmonary flow on CXR

Systolic cooing murmur right ventricle}

History of cardiac murmur

Cordlomegoly on CXR

Left atrium hypertrophy on EGG

A V S D

V S D

c l

c2 c 3 c 4 c J c (c 7 c 8 c 9 p ’*i

Left Down's Increased Systolic anterior syndrom e pulm onorycoolng

hem I flow on murmur

b lo c k a g e CXR right

ventricle

> ' c2'c 3c ^c 5c 6'c 7c 8c9' " '

mm

Left Down's Increased Systolic anterior syndrom e pulm onorycoolng

hemi flow on murmur

b lo c k a g e CXR right

ventricle

pathways punished pathways rewarded

Figure 5.11: The punishment and rewards computed by an AVSD and a VSD network when case AVSD7 is presented

In order to simplify the drawing, the network has been separated into two small networks, both with the same structure. The input and intermediate nodes in both networks represent the same groupings of findings. The difference between them is that the output node of the first network represents the AVSD diagnosis, while the output node of the second network represents the VSD diagnosis.

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When presented with the case AVSD7, all the connections of the AVSD network are activated, leading to the correct diagnosis. The connections of the VSD network are also activated, but they lead to an incorrect diagnosis (VSD instead of AVSD). Because of this the connections {cl, c2,..., clO} are rewarded, while {cF, c T c l O ’} are punished.

After the training period, the connections which have a larger number of punishments than rewards are removed from the network. In the given example, the connections {cl, c2, c3, c5, c7} still remain in the AVSD network, having reward values higher than punishment values. The connections {cS’, clO’} have not been rewarded in observation of case AVSD7. However, the findings represented in these connections are very common in VSD cases, which causes a high number of rewards to be computed by the connections. Table 5.2 shows the final number of punishments and rewards for these remaining connections, after the complete training period (66 training examples).

cl c2 c3 c5 c7 c8’ clO’

rewards 8.55 7.22 10.26 10.26 7.22 15.4 13

punishments 0 0 0 1.71 1.44 0.85 0.72

weight 0.93 0.89 1 0.83 0.56 1 0.84

Table 5.2: The punishments and rewards for the AVSD and the VSD networks

The punishment and reward accumulators of the network are then normalised into connection weights between the values 0 and 1. The following information can be observed or concluded from the table:

The connection c3 (from Left anterior hemi blockage to AVSD) obtained the highest weight for the AVSD diagnosis (1.0), which indicates that, when present, this finding has a very high importance for the identification of AVSD. For the VSD problem, the connection c8' obtained the highest weight.

The nodes connecting the Left anterior hemi blockage to other findings (Down’s syndrome and Increased pulmonary flow on CXR) also obtained high connection

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weights (connections c l and c2).

• The finding Increased pulmonary flow on CXR, although frequent in AVSD or VSD problems, had its connections pruned both from the VSD and the AVSD network. This is justified by the fact that this finding is not specific to any of the diagnoses, and on its own it cannot indicate any of them. However, the finding Increased pulmonary flow on CXR appears in other nodes, where it is combined with Left anterior hemi blockage, as well as with Systolic cooing murmur right ventricle . This shows that the combination of Increased pulmonary flow on CXR with Left anterior hemi blockage is both frequent and speccific to AVSD, which is important for the identification of the diagnosis. When combined with Systolic cooing murmur right ventricle, Increased pulmonary flow on CXR is frequent and specific to VSD problems.

The construction of the diagnosis descriptor for the AVSD disease is shown in figure 5.12.

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AVSD

W = 1 w=0.8

r= 10 w =0.9 w =0.8 w=0.8 w=0.8 w -0 .8 r 4.3^

Left Down's Increased Cardiom egaly Right History Left Tachypnea anterior syndrome pulm o iw y on CXR ventricle of cardiac ventricle

hemi flow on hypertrophy m um iur hypertrophy

blockage CXR

D ia g n o s is d e sc r ip to r ^ AVSD Trigger: Left anterior hemi blockage______ Primary: Down's syndrome

Increased pulmunary flow on CXR ________ Cardiomegaly on CXR__________

Secondary: Right ventricle hypertrophy History of cardiac murmur Left ventricle hypertrophy Tachypnea

Figure 5.12: The construction of the diagnosis descriptor of AVSD

The finding with the highest connection weight (w) is selected as the trigger of the diagnosis (ie. Left anterior hemi blockage). Then, other findings that appear in nodes with a high reward accumulator (r) are selected as primary findings. In the example, the nodes combining the finding Left anterior hemi blockage with Down s .syndrome, and with Increased pulmonary flow on CXR, are selected to form the primary findings. The node combining Cardiomegaly on CXR with Down's syndrome is also selected for its high frequency in AVSD cases (r=7.2, the same as Left anterior hemi blockage combined with Increased pulmonary flow on CXR). Other findings that appear in strong nodes are selected as secondary findings. For example, the finding Tachypnea, which appears in a node with Down's syndrome, is selected as a secondary finding.

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It is important to remark here that, by eliciting the information above, we convert the ‘opaque’ knowledge stored in the network into meaningful attributes, which rank the importance of findings in determining the solutions of the problems addressed. This more intelligible representation can be used for several purposes, such as for consultation and for building explanations.