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Analysis of Diabetics Data By Data Mining Techniques

V. Vallinayagam, N. Senthil Vel Murugan, K. Senthamarai Kannan

Abstract


Medical dataset is a vital ingredient used in predicting patient’s health condition. In other to have the best Prediction, there calls for a technique with high degree of accuracy. With the computerization in hospitals, a huge amount of data is collected. Although human decision-making is often optimal, it is poor when there are huge amounts of data to be classified. Medical data mining has great potential for exploring hidden patterns in the data sets of medical domain. These patterns can be used for clinical diagnosis   In this paper, we modeled data from diabetes patients and used it to predict the diabetes probability of any patient. In this paper a method for constructing fuzzy membership functions from data collected from Hospitals and diagnoses in the medical application area of diabetics is being presented. Fuzzy membership functions are generated to represent linguistic medical concepts for the data to symbol conversion unit of the medical knowledge based system. This data consists of six variables collected from private hospital in kanyakumari district. The variables chosen are low, medium and high. Based on the variables the risk of diabetics can be diagnosed. The aim of this paper is analyze the Diabetics data and Mamdani’s fuzzy inference system is used.  The reasonable results verify the validity of our method. This method tries to use the data mining technique effectively than other models available. Moreover the variables taken are linguistic variables which focuses on accuracy of the results.

Keywords


Data Mining, Diabetics, Membership Function and Fuzzy Inference System

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References


Ainsworth, Dean, Approximate inference for disease mapping. Comput. Statist. Data Anal. 50, pp.2552–2570, 2006.

Arnold, B.F., Gerke, O., Testing fuzzy linear hypotheses in linear regression models. Metrika 57, pp.81–95, 2003.

Atanassov, K., Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag, Berlin, 1999.

Berk., “Data Mining within a Regression Framework”, in Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Oded Maimon and Lior Rokach (eds.), Kluwer Academic Publishers, 2004.

Chang Su Lee, “A Framework of Adaptive T-S type Rough Fuzzy Inference Systems”, Ph.D thesis, School of Electrical Electronic and Computer Engineering, the University of Western Australia, 2009.

Dunham, M.H., “Data mining: Introductory and advanced topics”. Prentice Hall, Upper Saddle River, New Jersey, USA, 2002.

Fayyad U.M, Piatetsky-Shapiro G, Smyth P and uthurusamy “Advances in Knowledge Discovery and Data Mining”, AAI/MIT Press, pp.181-203, 1996.

Geetanjali Bhosale, Kamath,, “Fuzzy inference system for teaching staff performance appraisal”, International journal of Computer and Information Technology, vol. 2, No. 3, pp. 381– 384, 2013.

George J. Klir, Bo Yuan, Fuzzy Sets and Fuzzy Logic, (Prentice Hall of India Pvt. Ltd, Second Indian Reprint, 2000).

Han and M. Kamber, “Data Mining: Concepts and Techniques. Morgan Kaufman, San Francisco, 2000.

James F, Brule, “Fuzzy Systems – A Tutorial”, http://www.ortechLowengr.com/fuzzy/tutor.txt, 1985.

Jim C. Bezdek. “Fuzzy Mathematics in Pattern Classification.” Cornell University, Ithaca, 1973.

Kantardzic and Mehmed, “Data Mining: Concepts, Models, Methods, and Algorithms”, John Wiley & Sons, 2003.

Laviolette, M., Seaman, J.W., Barrett, J.D., Woodall, W.H., .A, probabilistic and statistical view of fuzzy methods. Technometrics 37, pp.249–292, 1995..

LeBlance, M., and Tibshirani, R., “Combining Estimates Regression and Classification.” Journal of the American Statistical Association, Vol.91,pp. 1641-1650, 1996.

Mamdani and Assilian. “An experiment in linguistic synthesis with a fuzzy logic controller”, International Journal of Man-Machine Studies, 1975.

Math Works, Fuzzy logic toolbox http://www.mathworks.com/products/Fuzzylogic, 2007.

Mehraban Sangatash M, Mohebbi M, Shahidi F, Vahidian Kamyad A, Qhods Rohani M, “Application of fuzzy logic to classify raw milk based on qualitative properties”, International journal of AgriScience, vol.2(12), pp.1168-1178, 2012.

Rajeswari, Vaithiyanathan, “Fuzzy based modeling for diabetic diagnostic decision support using Artificial Neural Network”, International Journal of Computer Science and Network Security, vol.11 No.4, pp. 126-130, 2011.

Renato Coppi, Maria A. Gil, Henk A.L. Kiers, “The fuzzy approach to statistical analysis”, Computational Statistics and Data Analysis, Elsevier, article in press, 2013.

World Health Organization, “WHO Expert Committee on Diabetics Mellitus”, Second Report, Geneva, World Health Org., Tech. Rep. Ser., no. 646, 1980.

Zalinda Othman, Khairanum Subari and Norhashimah, “Application of Fuzzy Inference Systems and Genetic Algorithms in Integrated Process Planning and Scheduling” International Journal of The Computer, The Internet and Management, Vol. 10, No2, pp. 81 – 96, 2002.

Zadeh, L.A.,” Fuzzy sets”, Information Control, vol. 8, pp. 338-358, 1965.


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