Assessment of Heart Disease using Fuzzy Classification Techniques

Abstract

In this paper we discuss the classification results of cardiac patients of ischemical cardiopathy, valvular heart disease, and arterial hypertension, based on 19 characteristics (descriptors) including ECHO data, effort testings, and age and weight. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering, and a new clustering technique, fuzzy hierarchical cross-classification. The characteristics clustering techniques produce fuzzy partitions of the characteristics involved and, thus, are useful tools for studying the similarities between different characteristics and for essential characteristics selection. The cross-classification algorithm produces not only a fuzzy partition of the cardiac patients analyzed, but also a fuzzy partition of their considered characteristics. In this way it is possible to identify which characteristics are responsible for the similarities or dissimilarities observed between different groups of patients.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

“…Additionally, for fuzzy PCA/LDA methods, a better compression, robustness towards outliers and more accurate data separation were demonstrated. [196][197][198][199][200] The main issues solved by employing fuzzy algorithms (fuzzy divisive hierarchical clustering, fuzzy cross-clustering, fuzzy PCA, 201,202 fuzzy LDA 203 ) are (i) classification and fingerprinting of spectral data 40,[204][205][206] and (ii) discrimination of multi-resistant common strains found under antibiotic/antifungal stress/susceptibility conditions. 48 Due to the original and complex approach, the results on SERS data from bacteria are scarce.…”