Evaluation of the Nutritional Effects of Fasting on Cardiovascular Diseases, Using Fuzzy Data Mining

Document Type : Research Paper

Authors

1 Department of Electrical, Faculty of Engineering, Islamic Azad University, Gonabad Branch, Gonabad, Iran

2 Department of Applied Mathematics,Faculty of Mathematics, Department of Electrical, Faculty of Engineering, Ferdowsi University, Mashhad, Iran.

3 Department of Medical Informatic, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

4 Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Introduction: Advances in information technology and data collection methods have enabled high-speed collection and storage of huge amounts of data. Data mining can be used to derive laws from large data volumes and their characteristics. Similarly, fuzzy logic by facilitating the understanding of events is considered a suitable complement to scientific data mining. Methods: The present study used clustering to identify the independent characteristics of data. Related fuzzy sets, linguistic variables, and data classifications were defined, and the index was introduced based on the characteristics extracted from useful results. By considering the disease risk factors, the results were analyzed. Results: Two factors contributing to the health improvement or deterioration were defined: ‘age’ and ‘the appropriateness or inappropriateness between insulin level and blood sugar’. In addition, according to the results, fasting had a positive effect on fatty substances of the blood (cholesterol and triglycerides). Conclusion: The results can help us determine whether or not an individual with a cardiovascular disease should fast in the month of Ramadan. However, due to variations in some features such as blood pressure throughout the day, there are uncertainties in some input data; therefore, the results could be far from reality. If it is possible to generate fuzzy data, then we can obtain more accurate results.

Keywords

References

  1. J. Cios K, Pedrycz W, W, Swiniarski R, A. Kurgan L. Data Mining, A Discovery Approach, New York: Springer Science+Business Media, LLC. (2007);
  2. L. Olson D, Delen D. Advanced Data Mining Techniques, Springer-Verlag Berlin Heidelberg. (2008);
  3. T. Larose D. Knowledge in Data, An Introduction to Data Mining, Hoboken, New Jersey: John Wiley & Sons, Inc. (2005);
  4. Han J, Kamber M. Data Mining: Concepts and Techniques, San Francisco: Morgan Kaufmann, Elsevier Inc. Second Edition. (2006);
  5. Berka P, Rauch J, Zighed D.A. Data Mining and Medical Knowledge Management: Cases and Applications, New York: Information Science Reference (an imprint of  IGI Global). (2009);
  6. Gan G, Ma C, Wu J. Data Clustering: Theory,Algorithms and Applications, Philadelphia: American Statistical Association and the Society for Industrial and Applied Mathematics. (2007);
  7. Chen G, Pham T. T. Introduction to Fuzzy Sets, Fuzzy Logic, And Fuzzy Control Systems, Florida: CRC Press LLC. (2001);
  8. Wang L, (Translated by: Teshneh Lab M, Saffar Pour N, Afyouni D.). A Course in Fuzzy Systems and Control, Tehran: Khajeh Nasir University of Technology, (2009); [Persian]
  9. Tanaka k, (Translated by: Vahidian Kamyad A, Tareghian H R.). An Introduction toFuzzy Logic for Practical Applications, Mashhad: Ferdowsi University of Mashhad, (2002); [Persian]
  10. Golyari S, Vahidian Kamyad A. Application of Fuzzy Logic in Medicine and Nutrition Science, The 2nd Conference on Medical Applications of Mathematics and Control Theory (2009). [Persian]
  11. Shahrabi J, Zolghadr Shojaei A. Advanced Data Mining: Concepts & Algorithms, Tehran: Iranian Academic Center for Education Culture and Research, AmirKabir Branch. (2009); [Persian]
  12. Yaghini M, Ranjpour M, Yousofi F. A Review of Fuzzy Clustering Algorithms, The 3rd Iran Data Mining Conference (2009). [Persian]
  13. Yang J, Watada J. Fuzzy Clustering Analysis of Data Mining: Application to An Accident Mining System, International Journal of Innovative Computing, Information and Control  (2012); Vol. 8, 8: 5715-5724.
  14. Yao K, Wang Y. The Applications of Fuzzy Clustering Analysis In The  Internal Structure of Perceptions of Organizational Politics, International Journal of  Innovative Management, Information & Production  (2011); Vol. 2, 4: 49-55.
  15. Dhar M. On Cardinality of Fuzzy Sets, International Journal of Intelligent Systems and Applications  (2013); 6: 47-52.
  16. Dhar M. Cardinality of Fuzzy Sets: An Overview, International Journal of Energy, Information and Communications  (2013); Vol. 4, 1: 15-22
  17. Viertl R. Statistical Methods for Fuzzy Data, Chichester: John Wiley & Sons Ltd. First edition. (2011).
  18. Buckingham S.A. Factors that Affect Cardiovascular Health: A Review, The Plymouth Student Scientist  (2007); 1, 1: 331-344.
  19. P Schnohr, J S Jensen, H Scharling, B E Nordestgaard. Coronary heart disease risk factors ranked by importance for the individual and community, European Heart Journal, (2002); 23: 620–626
  20. J P Degaute, P van de Borne, P Linkowski, E Van Cauter. Quantitative analysis of the 24-hour blood pressure and heart rate patterns in young men, Journal of American Heart Association,  (1991); 18:199-210
Journal of Nutrition,Fasting and Health
Volume 2, Issue 1 - Serial Number 1
February 2014
Pages 14-21

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History

  • Receive Date: 24 January 2014
  • Revise Date: 05 February 2014
  • Accept Date: 07 February 2014

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