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Saad, F. H., de la Iglesia, B. 
ORCID: https://orcid.org/0000-0003-2675-5826 and Bell, G. D.
(2006)
Effect of Document Representation on the Performance of Medical Document Classification.
In: 2006 International Conference on Data Mining, 2006-06-26 - 2006-06-29.
Abstract
Text classification in the medical domain is a real world problem with wide applicability. This paper investigates extensively the effect of text representation approaches on the performance of medical document classification. To accomplish this objective, we evaluated seven different approaches to represent real word medical documents. The text representation approaches investigated in this paper are basic word representation (bag-of-words), key-phrases, collocation extracted from preprocessed text, collocation extracted from postprocessed text, single-word-nouns, combination of singleword-noun and adjectives and combination of single-wordnoun, adjective and verbs. A set of experiments was conducted to make comprehensive evaluation of the effects of these representation approaches using real world medical documents by measuring the classification performance. We measured classification performance with information retrieval metrics; precision, recall, F-measure and accuracy. Our experimental results show that bag-ofwords representation outperforms all other representation approaches. In addition, careful use of selected features improve the classification performance.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 14 Jun 2011 08:00 |
| Last Modified: | 24 Sep 2024 07:11 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/22767 |
| DOI: |
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