Classification of Diabetes Mellitus using Modified Particle Swarm Optimization and Least Squares Support Vector Machine

International Journal of ComputerTrends and Technology (IJCTT)          
© 2014 by IJCTT Journal
Volume-8 Number-1                          
Year of Publication : 2014
Authors : Omar S.Soliman , Eman AboElhamd
DOI :  10.14445/22312803/IJCTT-V8P108


Omar S.Soliman , Eman AboElhamd. "Classification of Diabetes Mellitus using Modified Particle Swarm Optimization and Least Squares Support Vector Machine". International Journal of Computer Trends and Technology (IJCTT) 8(1):38-44, February 2014. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Diabetes Mellitus is a major health problem all over the world. Many classification algorithms have been applied for its diagnoses and treatment. In this paper, a hybrid algorithm of Modified-Particle Swarm Optimization and Least Squares-Support Vector Machine is proposed for the classification of type II DM patients. LS-SVM algorithm is used for classification by finding optimal hyper-plane which separates various classes. Since LS-SVM is so sensitive to the changes of its parameter values, Modified-PSO algorithm is used as an optimization technique for LS-SVM parameters. This will Guarantee the robustness of the hybrid algorithm by searching for the optimal values for LS-SVM parameters. The pro-posed Algorithm is implemented and evaluated using Pima Indians Diabetes Data set from UCI repository of machine learning databases. It is also compared with different classifier algorithms which were applied on the same database. The experimental results showed the superiority of the proposed algorithm which could achieve an average classification accuracy of 97.833%.

[1] Centers for disease control and prevention, national diabetes, fact sheet, (2011).
[2] World health organization, diabetes center, fact sheet n312, (2011).
[3] Qeethara Kadhim Al-Shayea. Artificial neural networks in medical diagnosis. International Journal of Computer Science, 8(2):150–154, 2011.
[4] Kurt George Matthew Mayer Alberti and PZ Zimmet. Defini-tion, diagnosis and classification of diabetes mellitus and its complications. part 1: diagnosis and classification of diabetes mellitus. provisional report of a who consultation. Diabetic medicine, 15(7):539–553, 1998.
[5] JC Bansal, PK Singh, Mukesh Saraswat, Abhishek Verma, Shimpi Singh Jadon, and Ajith Abraham. Inertia weight strategies in particle swarm optimization. In Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on, pages 633–640. IEEE, 2011.
[6] James Blondin. Particle swarm optimization: A tutorial. from site: http://cs. armstrong. edu/saad/csci8100/pso tutorial. pdf, 2009.
[7] Mehmet Recep Bozkurt, Nil¨ufer Yurtay, Ziynet Yilmaz, and Cengiz Sertkaya. Comparison of different methods for determining diabetes disease, 2013.
[8] SETTING IN DIABETES MELLITUS CARE. Guidelines for improving the care of the older person with diabetes mellitus. Hypertension, 16:2, 2003.
[9] 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.
[10] A Keech, RJ Simes, P Barter, J Best, R Scott, Marja-Riitta Taskinen, P Forder, A Pillai, T Davis, P Glasziou, et al. Effects of long-term fenofibrate therapy on cardiovascular events in 9795 people with type 2 diabetes mellitus (the field study): randomised controlled trial.Lancet, 366(9500):1849– 1861, 2005.
[11] Mehdi Khashei, Saeede Eftekhari, and Jamshid Parvizian. Diagnosing diabetes type ii using a soft intelligent binary classification model. Review of Bioinformatics and Biomet-rics, 1(1), 2012.
[12] Alexis Marcano-Cede˜no, Joaqu´in Torres, and Diego Andina. A prediction model to diabetes using artificial metaplasticity. In New Challenges on Bioinspired Applications, pages 418– 425. Springer, 2011.
[13] 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.
[14] Afsaneh Morteza, Manouchehr Nakhjavani, Firouzeh As-garani, Filipe LF Carvalho, Reza Karimi, and Alireza Es-teghamati. Inconsistency in albuminuria predictors in type2 diabetes: a comparison between neural network and conditional logistic regression. Translational Research, 2013.
[15] KJ Mukamal, JR Kizer, L Djouss´e, JH Ix, S Zieman, DS Siscovick, CT Sibley, RP Tracy, and AM Arnold. Prediction and classification of cardiovascular disease risk in older adults with diabetes. Diabetologia, 56(2):275–283, 2013.
[16] Elin Olafsdottir, Thor Aspelund, Gunnar Sigurdsson, Rafn Benediktsson, Bolli Thorsson, Tamara B Harris, Lenore J Launer, Gudny Eiriksdottir, and Vilmundur Gudnason. Sim-ilar 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.
[17] Rachel O’Reilly. Cross-validation for model selection in model-based clustering. 2012.
[18] R Priya and P Aruna. Review of automated diagnosis of diabetic retinopathy using the support vector machine. Inter-national Journal of Applied Engineering Research, Dindigul, 1(4):844–863, 2011.
[19] Rashedur M Rahman and Farhana Afroz. Comparison of various classification techniques using different data mining tools for diabetes diagnosis. 2013.
[20] Devang Odedra Shankaracharya, Subir Samanta, and Ambar-ish S Vidyarthi. Computational intelligence in early diabetes diagnosis: a review. The review of diabetic studies: RDS, 7(4):252, 2010.
[21] Devang Odedra Shankaracharya, Subir Samanta, and Ambar-ish S Vidyarthi. Computational intelligence-based diagnosis tool for the detection of prediabetes and type 2 diabetes in india. The review of diabetic studies: RDS, 9(1):55, 2012.
[22] Xigao Shao, Kun Wu, and Bifeng Liao. Single directional smo algorithm for least squares support vector machines. Computational intelligence and neuroscience, 2013, 2013.
[23] Yuhui Shi et al. Particle swarm optimization: developments, applications and resources. In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, volume 1, pages 81–86. IEEE, 2001.
[24] Johan AK Suykens and Joos Vandewalle. Least squares support vector machine classifiers. Neural processing letters, 9(3):293–300, 1999.
[25] Peter Tsyurmasto, Michael Zabarankin, and Stan Uryasev. Value-at-risk support vector machine: Stability to outliers. 2013.
[26] Jaakko Tuomilehto, Jaana Lindstr¨om, Johan G Eriks-son, Timo T Valle, Helena H¨am¨al¨ainen, Pirjo Ilanne-Parikka, Sirkka Kein¨anen-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.
[27] 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.
[28] 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.
[29] Weiwei Wang, Jie Cao, Hongke Lu, and Jian Wang. A default discrimination method for manufacturing companies by improved pso-based ls-svm.
[30] J-F Yale, George Bakris, Bertrand Cariou, Dennis Yue, Elias David-Neto, Liwen Xi, Katherine Figueroa, Ewa Wajs, Keith Usiskin, and Gary Meininger. Efficacy and safety of canagliflozin in subjects with type 2 diabetes and chronic kidney disease. Diabetes, Obesity and Metabolism, 2013.
[31] Jieping Ye and Tao Xiong. Svm versus least squares svm. In International Conference on Artificial Intelligence and Statistics, pages 644–651, 2007.

Diabetes Mellitus (DM), Particle Swarm Optimization (PSO), Least Squares Support Vector Machine (LS-SVM).