Fuzzy and hard clustering analysis for thyroid disease

Thyroid hormones produced by the thyroid gland help regulation of the body's metabolism. A variety of methods have been proposed in the literature for thyroid disease classification. As far as we know, clustering techniques have not been used in thyroid diseases data set so far. This paper prop...

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Veröffentlicht in:	Computer methods and programs in biomedicine Jg. 111; H. 1; S. 1 - 16
Hauptverfasser:	Azar, Ahmad Taher, El-Said, Shaimaa Ahmed, Hassanien, Aboul Ella
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ireland Ltd 01.07.2013 Elsevier
Schlagworte:	Algorithms Applied sciences Artificial Intelligence Biological and medical sciences Cluster Analysis Computer science; control theory; systems Computerized, statistical medical data processing and models in biomedicine Data processing. List processing. Character string processing Databases, Factual Diagnosis, Computer-Assisted - statistics & numerical data Endocrinopathies Exact sciences and technology Fuzzy C-means Fuzzy Logic Gath–Geva algorithm Gustafson–Kessel algorithm Humans Internal Medicine K-means clustering K-medoids clustering Medical sciences Memory organisation. Data processing Models and simulation Other Pattern Recognition, Automated Software Thyroid disease Thyroid Diseases - classification Thyroid Diseases - diagnosis Thyroid. Thyroid axis (diseases) Gustafson–Kessel algorithm Fuzzy C-means K-medoids clustering Thyroid disease Gath–Geva algorithm K-means clustering Cluster analysis Data analysis Gustafson-Kessel algorithm Thyroid diseases Cluster Metabolic diseases K means algorithm Thyroid gland K medoids algorithm Metabolism Optimization Fuzzy logic Experimental result Multidimensional analysis Fuzzy algorithm Number Classification Gath―Geva algorithm Selection criterion
ISSN:	0169-2607, 1872-7565, 1872-7565
Online-Zugang:	Volltext
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