Diagnosing patients with a combination of principal component analysis and case based reasoning
| Autor: | Beatriz López, Carles Pous, Dani Caballero |
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| Rok vydání: | 2009 |
| Předmět: |
Alternative methods
Simplex business.industry Computer science Computation Machine learning computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Simple (abstract algebra) Hybrid system Principal component analysis Case-based reasoning Artificial intelligence Data mining business computer Categorical variable |
| Zdroj: | International Journal of Hybrid Intelligent Systems. 6:111-122 |
| ISSN: | 1875-8819 1448-5869 |
| DOI: | 10.3233/his-2009-0090 |
| Popis: | This paper addresses the application of a principal component analysis (PCA) of categorical data prior to diagnosing a patients dataset using a case-based reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical dataset contains many categorical data and alternative methods such as RS-PCA are required. Thus, we propose to hybridize RS-PCA (regular simplex PCA) and a simple CBR system. Results show how the hybrid system, when diagnosing a medical dataset, produces results similar to the ones obtained when using the original attributes. These results are quite promising since they allow diagnosis with less computation effort and memory storage. |
| Databáze: | OpenAIRE |
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