CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Chaotic Levy Flights Bat Algorithm for Diagnosing Diabetes Mellitus

by Omar S. Soliman, Eman Abo Elhamd
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 1
Year of Publication: 2015
Authors: Omar S. Soliman, Eman Abo Elhamd
10.5120/19505-1103

Omar S. Soliman, Eman Abo Elhamd . A Chaotic Levy Flights Bat Algorithm for Diagnosing Diabetes Mellitus. International Journal of Computer Applications. 111, 1 ( February 2015), 36-42. DOI=10.5120/19505-1103

@article{ 10.5120/19505-1103,
author = { Omar S. Soliman, Eman Abo Elhamd },
title = { A Chaotic Levy Flights Bat Algorithm for Diagnosing Diabetes Mellitus },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 1 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number1/19505-1103/ },
doi = { 10.5120/19505-1103 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:46:45.367026+05:30
%A Omar S. Soliman
%A Eman Abo Elhamd
%T A Chaotic Levy Flights Bat Algorithm for Diagnosing Diabetes Mellitus
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 1
%P 36-42
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Bat algorithm is a meta-heuristic algorithm that is based on the echolocation behavior of bats. The searching behavior of the algorithm depends on generating uniformly distributed random walks in the search space. Hence, it may suffer from being tapped in local optima. In this paper, a classification using Bat inspired algorithm with chaotic levy flight variable is proposed. The chaotic variable has set of characteristics that enable it to enrich the searching behavior and prevent the Bat algorithm from being trapped into local optimum. The chaotic sequence and a chaotic Levy flight are incorporated with Bat algorithm for many purposes including, efficiently generating new solutions via randomization, increase the diversity of the solutions, avoid trapping in a local optimum and increase the chances of finding global optimum solution. The proposed algorithm aims to help physicians in early diagnosis and treatment of Diabetes Mellitus (DM). DM is a major health problem in both industrial and developing countries and its incidence is rising. The proposed algorithm is applied on Pima Indians Diabetes data set from UCI repository of machine learning data bases. The experimental results prove the superiority of the proposed algorithm over the traditional Bat algorithm as well as different classifiers which were implemented on the same data set and within the same environment.

References
  1. World health organization, diabetes center, fact sheet n312, www. who. int/mediacentre/factsheets/fs312/en (2011).
  2. American Diabetes Association. "Diagnosis and classification of diabetes mellitus. " Diabetes care 31. Supplement 1 (2008): S55-S60. ?
  3. SETTING IN DIABETES MELLITUS CARE. Guidelines for improving the care of the older person with diabetes mellitus. Hypertension, 16:2, 2003.
  4. Jaakko Tuomilehto, Jaana Lindstrom, Johan G Eriksson, Timo T Valle, Helena Hamalainen, PirjoIlanne-Parikka, Sirkka Keinanen-Kiukaanniemi, Mauri Laakso, Anne Louheranta, Merja Rastas, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. New England Journal of Medicine, 344(18):1343-1350, 2001.
  5. Yang, Xin-She, Suash Deb, and Simon Fong. "Bat Algorithm is Better Than Intermittent Search Strategy. " JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 22. 3 (2014): 223-237.
  6. Mishra, Sashikala, Kailash Shaw, and Debahuti Mishra. "A new meta-heuristic bat inspired classification approach for microarray data. " Procedia Technology 4 (2012): 802-806. ?
  7. Lin, Jiann-Horng, et al. "A chaotic Levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems, J. " Computer and Information Technology 2. 2 (2012): 56-63. ?
  8. Yang, Xin-She. "Bat algorithm and cuckoo search: a tutorial. " Artificial Intelligence, Evolutionary Computing and Metaheuristics. Springer Berlin Heidelberg, 2013. 421-434. ?
  9. James Blondin. Particle swarm optimization: A tutorial. from site: http://cs. armstrong. edu/saad/csci8100/pso tutorial. pdf, 2009.
  10. Qeethara Kadhim Al-Shayea. Artificial neural networks in medical diagnosis. International Journal of Computer Science, 8(2):150–154, 2011.
  11. Mehdi Khashei, Saeede Eftekhari, and Jamshid Parvizian. Diagnosing diabetes type ii using a soft intelligent binary classification model. Review of Bioinformatics and Biometrics, 1(1), 2012.
  12. Afsaneh Morteza, Manouchehr Nakhjavani, Firouzeh Asgarani, Filipe LF Carvalho, Reza Karimi, and Alireza Esteghamati. Inconsistency in albuminuria predictors in type 2 diabetes: a comparison between neural network and conditional logistic regression. Translational Research, 2013.
  13. P Venkatesan and S Anitha. Application of a radial basis function neural network for diagnosis of diabetes mellitus. CURRENT SCIENCE-BANGALORE-, 91(9): 1195, 2006.
  14. Alexis Marcano-Cede˜no, Joaqu´?n Torres, and Diego Andina. A prediction model to diabetes using artificial metaplasticity. In New Challenges on Bioinspired Applications, pages 418–425. Springer, 2011.
  15. Chongjian Wang, Linlin Li, Ling Wang, Zhiguang Ping, Muanda Tsobo Flory, Gaoshuai Wang, Yuanlin Xi, and Wenjie Li. Evaluating the risk of type 2 diabetes mellitus using artificial neural network: An effective classification approach. Diabetes research and clinical practice, 2013
  16. Mehmet Recep Bozkurt, Nil¨ufer Yurtay, Ziynet Y?lmaz, and Cengiz Sertkaya. Comparison of different methods for determining diabetes disease, 2013.
  17. Devang Odedra Shankaracharya, Subir Samanta, and Ambarish S Vidyarthi. Computational intelligence in early diabetes diagnosis: a review. The review of diabetic studies: RDS, 7(4):252, 2010.
  18. Elodie Scaglia, Ganesh D Sockalingum, Juergen Schmitt, Cyril Gobinet, Nathalie Schneider, Michel Manfait, and G´erard Thi´efin. Noninvasive assessment of hepatic fibrosis in patients with chronic hepatitis c using serum fourier transform infrared spectroscopy. Analytical and bioanalytical chemistry, 401(9):2919–2925, 2011.
  19. Peter Tsyurmasto, Michael Zabarankin, and Stan Uryasev. Value-at-risk support vector machine: Stability to outliers. 2013.
  20. Javad Haddadnia Hadi Varharam Mohammad Fiuzy, Azam Qarehkhani. Introduction of a method to diabetes diagnosis according to optimum rules in fuzzy systems based on combination of data mining algorithm (d-t), evolutionary algorithms (aco) and artificial neural networks (nn). The Journal of Mathematics and Computer Science (JMCS), 6(4):272–285, 2013.
  21. Elin Olafsdottir, Thor Aspelund, Gunnar Sigurdsson, Rafn Benediktsson, Bolli Thorsson, Tamara B Harris, Lenore J Launer, Gudny Eiriksdottir, and Vilmundur Gudnason. Similar decline in mortality rate of older persons with and without type 2 diabetes between 1993 and 2004 the icelandic population-based reykjavik and ages-reykjavik cohort studies. BMC public health, 13(1):36, 2013.
  22. R Karak?¸s, M Tez, YA K?l?¸c, Y Kuru, and I G¨uler. A genetic algorithm model? based on artificial neural network for prediction of the axillary lymph node status in breastcancer. Engineering Applications of Artificial Intelligence, 26(3):945–950, 2013.
  23. Mehdi Khashei, Saeede Eftekhari, and Jamshid Parvizian. Diagnosing diabetes type ii using a soft intelligent binary classification model. Review of Bioinformatics and Biometrics, 1(1), 2012.
  24. Rachel O'Reilly, Cross-validation for model selection in model-based clustering, 2012.
  25. Catalin Stoean, Ruxandra Stoean, Monica Lupsor, Horia Stefanescu, and Radu Badea. Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis. Computers in Biology and Medicine, 41(4):238–246,2011.
  26. Kayaer, Kamer, and Tulay Y?ld?r?m. "Medical diagnosis on Pima Indian diabetes using general regression neural networks. " Proceedings of the international conference on artificial neural networks and neural information processing (ICANN/ICONIP). 2003. ?
  27. Latif, Aadil, and Peter Palensky. "Economic Dispatch Using Modified Bat Algorithm. " Algorithms 7. 3 (2014): 328-338. ?
  28. Rashedur M Rahman and Farhana Afroz. Comparison of various classification techniques using different data mining tools for diabetes diagnosis. 2013
  29. Asha Gowda Karegowda, AS Manjunath, and MA Jayaram. Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima indians diabetes. International Journal on Soft Computing, 2(2):15–23, 2011
Index Terms

Computer Science
Information Sciences

Keywords

Bat Inspired Algorithm (BIA) Levy Flight Chaotic Variable Diabetes Mellitus (DM).

Powered by PhDFocusTM