Diagnosing patients with a combination of principal component analysis and case based reasoning

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.