Automatic Fuzzy Classification System for Metabolic Types Detection

Patients suffering from obesity have different demands for medical treatment regarding the causes of their metabolic disorders. To propose new medical solutions to weight reduction, it is desirable to group patients exhibiting similar characteristics. This contribution describes an automatic fuzzy classification system capable of dividing obese patients into groups of diverse metabolic types. Metabolic data were acquired through energometry tests and bioimpedance measurements. Methods considered in this paper are particularly Principal Component Analysis used for data set’s reduction and fuzzy clustering method dividing patients into groups called clusters. Newly tested patients are then classified into designed clusters. A set of statistical hypothesis testing methods is eventually applied to verify the performed classification. The designed classification system could be applied in hospitals to help the doctors with design of an individual treatment for obese patients’ groups.

[1]  Saleem A. Kassam.  Elements of Statistical Hypothesis Testing , 1988 .

[2]  Bojan Novak,et al.  Childhood obesity prediction with artificial neural networks , 1996, Proceedings Ninth IEEE Symposium on Computer-Based Medical Systems.

[3]  I. Jolliffe.  Principal Component Analysis , 2002 .

[4]  R.J. Almeida,et al.  Comparison of fuzzy clustering algorithms for classification , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[5]  Mohammad Reza Rajati,et al.  Unit Sizing of a Stand-Alone Hybrid Power System Using Model-Free Optimization , 2007 .

[6]  Balazs Feil,et al.  Cluster Analysis for Data Mining and System Identification , 2007 .

[7]  Tzung-Pei Hong,et al.  On Genetic-Fuzzy Data Mining Techniques , 2007 .

[8]  Yan Ren,et al.  A New Method for Fuzzy Clustering Analysis Based on AFS Fuzzy Logic , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[9]  Han Jing.  Application of Fuzzy Data Mining Algorithm in Performance Evaluation of Human Resource , 2009, 2009 International Forum on Computer Science-Technology and Applications.

[10]  Nasser Sadati,et al.  Fuzzy clustering means data association algorithm using an adaptive neuro-fuzzy network , 2009, 2009 IEEE Aerospace conference.

[11]  Abu Sayed Md. Latiful Hoque,et al.  Clustering medical data to predict the likelihood of diseases , 2010, 2010 Fifth International Conference on Digital Information Management (ICDIM).

[12]  D. Vanisri,et al.  Fuzzy pattern cluster scheme for breast cancer datasets , 2010, 2010 International Conference on Communication and Computational Intelligence (INCOCCI).

[13]  Binu Thomas,et al.  A fuzzy threshold based unsupervised clustering algorithm for natural data exploration , 2010, 2010 International Conference on Networking and Information Technology.

[14]  Juebo Wu,et al.  An Improved Fuzzy Clustering Method for Text Mining , 2010, 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing.

[15]  Tamas Ferenci,et al.  Effects of obesity: A multivariate analysis of laboratory parameters , 2011, 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI).

[16]  Michal Prauzek,et al.  A hybrid device for electrical impedance tomography and bioelectrical impedance spectroscopy measurement , 2014, 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE).

[17]  Michal Prauzek,et al.  Fuzzy clustering method for large metabolic data set by statistical approach , 2014, 2014 Cairo International Biomedical Engineering Conference (CIBEC).