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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Vydáno v:Computer methods and programs in biomedicine Ročník 111; číslo 1; s. 1 - 16
Hlavní autoři: Azar, Ahmad Taher, El-Said, Shaimaa Ahmed, Hassanien, Aboul Ella
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ireland Ltd 01.07.2013
Elsevier
Témata:
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
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Shrnutí: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 proposes a comparison between hard and fuzzy clustering algorithms for thyroid diseases data set in order to find the optimal number of clusters. Different scalar validity measures are used in comparing the performances of the proposed clustering systems. To demonstrate the performance of each algorithm, the feature values that represent thyroid disease are used as input for the system. Several runs are carried out and recorded with a different number of clusters being specified for each run (between 2 and 11), so as to establish the optimum number of clusters. To find the optimal number of clusters, the so-called elbow criterion is applied. The experimental results revealed that for all algorithms, the elbow was located at c=3. The clustering results for all algorithms are then visualized by the Sammon mapping method to find a low-dimensional (normally 2D or 3D) representation of a set of points distributed in a high dimensional pattern space. At the end of this study, some recommendations are formulated to improve determining the actual number of clusters present in the data set.
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ISSN:0169-2607
1872-7565
1872-7565
DOI:10.1016/j.cmpb.2013.01.002