The preliminary tests performed using the prototype system developed suggest that the MCRDR method, with substantial modification, can be applied successfully to Knowledge Discovery tasks. This new method of Exposed MCRDR allowed an expert in the domain of Lung Function to discover and provide some measure of evidence for knowledge that the expert was not previously aware of, in a much shorter time than the manual process which would previously have been performed. However no comparison has been made to other Knowledge Discovery methods – while many other methods are not applicable to the types of domain which EMCRDR was designed for, until a comparison is made the real benefit of the method to the field of Knowledge Discovery cannot be accurately measured, although the simple fact that this kind of tool is rare for domains such as the one considered suggests that this study may be of significant value.
Although the method has been shown to work, there are a number of improvements that may enhance the applicability and effectiveness of the method for Knowledge Discovery purposes. In particular the assistance provided to the expert in discovering trends in the data is an area that can always be diversified and improved, until the point at which the extra information about the data provided becomes too large for the expert to be able to easily comprehend. The confusion of the expert in using the case-based approach to Knowledge Acquisition also denotes another area that can be improved: allowing the expert more freedom in defining rules may alleviate the problem. The other option is to provide a more detailed and thorough explanation to the expert of how the process functions, although results suggest that this would not resolve the issue.
One of the issues raised by this system was that the method does require that the expert have a level of knowledge about how the process works beyond that which is normally required by an MCRDR Knowledge Acquisition method. However, the proficiency of the expert was shown to increase to a sufficient level in a very short space of time, so that the expert was able to make effective use of the system without anything more than basic instruction and experimentation. It can also be argued that
any other Knowledge Discovery method would also require a familiarisation period at least equal to that of this method, but this is unproven conjecture.
Perhaps the most significant result to be attained from this study is not that a successful knowledge base was produced using this method, including new knowledge, but rather the process by which that knowledge base was constructed, and what the implications are for MCRDR Knowledge Acquisition. The extra functionality allowed to the expert in developing the knowledge base had a dramatic effect on how the Knowledge Acquisition was performed: allowing the expert to edit rules, combined with viewing the dataset, fundamentally altered the manner in which the expert approached the Knowledge Acquisition from a case-based perspective to a rule-based perspective. This would confirm previous literature that suggests that allowing rule editing would not be of benefit in Knowledge Acquisition for expert system development. However if the goal is to develop a knowledge base that is readable and useful to a human, these features show a definite positive move towards this goal. A possible disadvantage is that they may detract from the accuracy of the system. Allowing rule deletions had little impact on this study, but a more thorough test might provide a better analysis of the usefulness of this feature.
The benefits of the method modifications are in some ways less clear than the disadvantages. While showing the knowledge base influences the method of its construction, it allows the expert to review the recorded knowledge to find missing knowledge, find areas to explore, and review flaws. Similarly while rule editing changes the perception of how to create the knowledge base, it allows the knowledge base to be expressed in a form which the expert can easily understand, enhancing (or possibly making plausible) the advantage of showing the knowledge base. It is theorised that allowing rule deletions would further enhance this ability, but the inherent risks in damaging the knowledge may outweigh the benefits. Further analysis needs to be performed to test this.
In final conclusion, despite remaining uncertainties about how the method should be implemented, it can be seen that the Exposed MCRDR method is a valuable Knowledge Discovery approach, even for domains which require extensive
complex and specific target knowledge. In the short time that an expert was using the method to model the domain and discover new data, three useful and previously unknown methods of classification were derived. However, determining the full potential of the method, and how to achieve that potential, requires significant further testing. The insights provided by this method into the MCRDR Knowledge Acquisition process also require further examination to determine their extent and applicability. The following section will discuss these future directions for the method and MCRDR Knowledge Acquisition.