Cardiac arrhythmia classification using self organizing MAP (SOM) - based ensemble model
Many clinical decision support systems have been using data mining techniques for prediction and diagnosis of various diseases with good accuracy. This is due to its ability to distinguish various patterns of data from its background, and make conclusions about the categories of the patterns. A l...
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| Main Author: | |
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| Format: | Final Year Project Report |
| Language: | English English |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/12251/1/Cardiac%20arrhythmia%20classification%20using%20self%20organizing%20MAP%20%28SOM%29-based%20ensemble%20model%20%2824%20pages%29.pdf http://ir.unimas.my/id/eprint/12251/8/Cardiac%20arrhythmia%20classification%20using%20self%20organizing%20MAP%20%28SOM%29-based%20ensemble%20model%20%28fulltext%29.pdf http://ir.unimas.my/id/eprint/12251/ |
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| Summary: | Many clinical decision support systems have been using data mining techniques for
prediction and diagnosis of various diseases with good accuracy. This is due to its ability to
distinguish various patterns of data from its background, and make conclusions about the
categories of the patterns. A large number of such systems have been widely used in the
diagnosis of heart diseases. One of the heart diseases in concern is cardiac arrhythmia. Most
systems used in diagnosing cardiac arrhythmia uses data mining techniques, like Artificial
Neural Networks, particularly in the form of a single classifier. In this project, a Self
Organizing Map (SOM) - Based Ensemble model is proposed for the classification of cardiac
arrhythmia disease dataset. An ensemble is a model that applies multiple learning models and
combining the outputs or predictions to solve a particular problem. An ensemble is stated to
predict or classify datasets more accurately than some single classifier models. The ensemble
consists of three SOM classifiers trained with different number of dimension. For the
ensemble, a voting technique is used to average the prediction of each single SOM classifier
to obtain the final prediction. The results displayed show that the SOM ensemble model has
higher classification accuracy than that of single SOM classifiers. Ensemble learning
eliminates errors of single classifiers by averaging the prediction of each classifier, thus
resulting in a more accurate output. |
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