This thesis advances the understanding of the application of artificial neural networks
ensemble to clinical data by addressing the following fundamental question: What is the
potentiality of an ensemble of neural networks models as a filter and classifier in a
complex clinical situation?
A novel neural networks ensemble classification model called Rules and
Information Driven by Consistency in Artificial Neural Networks Ensemble (RIDCANNE)
is developed for the purpose of prediction of medical outcomes or events, such
as kidney transplants. The proposed classification model is based on combination of
initial data preparations, preliminary classification by ensembles of Neural Networks,
and generation of new training data based on criteria of highly accuracy and model
agreement. Furthermore, it can also generate decision tree classification models to
provide classification of data and the prediction results. The case studies described in
this thesis are from a kidney transplant database and two well-known collections of
benchmark data known as the Pima Indian Diabetes and Wisconsin Cancer datasets. An
implication of this study is that further attention needs to be given to both data
collection and preparation stages. This study revealed that even neural network
ensemble models that are known for their strong generalization ability might not be able
to provide a high level of accuracy for complex, noisy and incomplete clinical data.
However, by using a selective subset of data points, it is possible to improve the overall
accuracy.
In summary, the research conducted for this thesis advances the current clinical
data preparation and classification techniques in which the task is to extract patterns that
contain higher information content from a sea of noisy and incomplete clinical data, and
build accurate and transparent classifiers. The RIDC-ANNE approach improves an
analyst�s ability to better understand the data. Furthermore, it shows great promise for
use in clinical decision making systems. It can provide us with a valuable data mining
tool with great research and commercial potential.
| Identifer | oai:union.ndltd.org:ADTP/219584 |
| Date | January 2007 |
| Creators | Shadabi, Fariba, N/A |
| Publisher | University of Canberra. Information Sciences & Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | ), Copyright Fariba Shadabi |
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