Machine learning method for knowledge discovery experimented with otoneurological data
| Autor: | Kirsi Varpa, Kati Iltanen, Martti Juhola |
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| Rok vydání: | 2008 |
| Předmět: |
Diagnostic Techniques
Otological Decision support system Evidence-Based Medicine Medical Records Systems Computerized Computer science business.industry Diagnostic Techniques Neurological Inference Health Informatics Evidence-based medicine Decision Support Systems Clinical Machine learning computer.software_genre Computer Science Applications Weighting Knowledge extraction Artificial Intelligence Vertigo Humans Diagnosis Computer-Assisted Artificial intelligence business Classifier (UML) computer Software |
| Zdroj: | Computer Methods and Programs in Biomedicine. 91:154-164 |
| ISSN: | 0169-2607 |
| Popis: | We have been interested in developing an otoneurological decision support system that supports diagnostics of vertigo diseases. In this study, we concentrate on testing its inference mechanism and knowledge discovery method. Knowledge is presented as patterns of classes. Each pattern includes attributes with weight and fitness values concerning the class. With the knowledge discovery method it is possible to form fitness values from data. Knowledge formation is based on frequency distributions of attributes. Knowledge formed by the knowledge discovery method is tested with two vertigo data sets and compared to experts' knowledge. The experts' and machine learnt knowledge are also combined in various ways in order to examine effects of weights on classification accuracy. The classification accuracy of knowledge discovery method is compared to 1- and 5-nearest neighbour method and Naive-Bayes classifier. The results showed that knowledge bases combining machine learnt knowledge with the experts' knowledge yielded the best classification accuracies. Further, attribute weighting had an important effect on the classification capability of the system. When considering different diseases in the used data sets, the performance of the knowledge discovery method and the inference method is comparable to other methods employed in this study. |
| Databáze: | OpenAIRE |
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