Rule extraction from support vector machines using ensemble learning approach: An application for diagnosis of diabetes

IEEE Journal of Biomedical and Health Informatics

Volumn 19, Issue 2, 2015, Pages 728-734

  Han, Longfei ^a   Luo, Senlin ^a   Yu, Jianmin ^a   Pan, Limin ^a   Chen, Songjing ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

diagnosis of diabetes; ensemble learning; random forest (RF); rule extraction; support vector machines (SVMs)

Indexed keywords

DECISION TREES; DIAGNOSIS; EXTRACTION; HYBRID SYSTEMS; RANDOM FORESTS; STATISTICAL METHODS; SUPPORT VECTOR MACHINES; SURVEYS;

DIABETES MELLITUS; ENSEMBLE LEARNING; ENSEMBLE LEARNING APPROACH; IMBALANCE PROBLEM; NUTRITION SURVEY; RULE EXTRACTION; SUPPORT VECTOR MACHINE (SVMS); WEIGHTED AVERAGES;

LEARNING SYSTEMS;

ADOLESCENT; ADULT; ARTIFICIAL INTELLIGENCE; COMPUTER ASSISTED DIAGNOSIS; DECISION TREE; DIABETES MELLITUS; FACTUAL DATABASE; HUMAN; PROCEDURES; REPRODUCIBILITY; SUPPORT VECTOR MACHINE; YOUNG ADULT;

ADOLESCENT; ADULT; ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; DECISION TREES; DIABETES MELLITUS; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINE; YOUNG ADULT;

EID: 84924674527     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2014.2325615     Document Type: Article

References (41)

1. D. Whiting, L. Guariguata, C. Weil, and J. Shaw, "IDF diabetes atlas: Global estimates of the prevalence of diabetes for 2011 and 2030, " Diabetes Res. Clin. Pract., vol. 94, pp. 311-321, 2011.
2. International Diabetes Federation. (2011). IDF Diabetes Atlas, 5th ed. Brussels, Belgium. [Online]. Available: http://www.idf.org/diabetesatlas
3. N. Brown, J. Critchley, P. Bogowicz, M. Mayige, and N. Unwin, "Risk scores based on self-reported or available clinical data to detect undiagnosed type 2 diabetes: A systematic review, " Diabetes Res. Clin. Pract., vol. 98, pp. 369-385, 2012.
4. D. Noble, R. Mathur, T. Dent, C. Meads, and T. Greenhalgh, "Riskmodels and scores for type 2 diabetes: Systematic review, " BMJ., vol. 343, pp. 1-31, 2011.
5. K. Heikes, D. Eddy, B. Arondekar, and L. Schlessinger, "Diabetes risk calculator: A simple tool for detecting undiagnosed diabetes and prediabetes, " Diabetes Care, vol. 31, no. 5, pp. 1040-1045, 2008.
6. S. J. Griffin, P. S. Little, C. N. Hales, A. L. Kinmonth, and N. J.Wareham, "Diabetes risk score: Towards earlier detection of type 2 diabetes in general practice, " Diabetes/Metabolism Res. Rev., vol. 16, pp. 164-171, 2000.
7. M. W. Hanif, G. Valsamakis, A. Dixon, A. Boutsiadis, A. F. Jones, A. H. Barnett, and S. Kumar, , "Detection of impaired glucose tolerance and undiagnosed type 2 diabetes in UK South Asians: An effective screening strategy, " Diabetes, Obesity Metabolism., vol. 10, no. 9, pp. 755-762, 2008.
8. D. B. Rolka, K. M. Narayan, T. J. Thompson, J. Lindenmayer, and D. O. Stuart, "Performance of recommended screening tests for undiagnosed diabetes and dysglycemia, " Diabetes Care, vol. 24, no. 11, pp. 1899-1903, 2001.
9. W. G. Gao, Y. H. Dong, Z. C. Pang, H. R. Nan, S. J. Wang, J. Ren, L. Zhang, J. Tuomilehto, and Q. Qiao, "A simple Chinese risk score for undiagnosed diabetes, " Diabetic Med., vol. 27, pp. 274-281, 2010.
10. T. Thitaporn, D. Newby, J. Schneider, and S. C. Li, "Survey of diabetes risk assessment tools: Concepts, structure and performance, " Diabetes/Metabolism Res. Rev., vol. 28, pp. 485-498, 2012.
11. B. Buijsse, R. Simmons, S. Griffin, and M. Schulze, "Risk assessment tools for identifying individuals at risk of developing type 2 diabetes, " Epidemiol. Rev., vol. 33, pp. 46-62, 2011.
12. P. E. H. Schwarz, J. Li, J. Lindstrom, and J. Tuomilehto, "Tools for predicting the risk of type 2 diabetes in daily practice, " Horm. Metab. Res., vol. 41, pp. 86-97, 2009.
13. L. Tapak, H. Mahjub, O. Hamidi, and J. Poorolajal, "Real-data comparison of data mining methods in prediction of diabetes in Iran, " Healthcare Informat. Res., vol. 19, no. 3, pp. 177-185, 2013.
14. C. M. Velu and K. R. Kashwan, "Visual data mining techniques for classification of diabetic patients, " in Proc. IEEE 3rd Int. Adv. Comput. Conf., 2013, pp. 1070-1075.
15. O. Akgobek, "A hybrid approach for improving the accuracy of classification algorithms in data mining, " Energy Edu. Sci. Technol. Part A-Energy Sci. Res., vol. 29, no. 2, pp. 1039-1054, 2012.
16. J. Lee, B. Keam, E. J. Jang, M. S. Park, J. Y. Lee, D. B. Kim, C. H. Lee, T. Kim, B. Oh, H. J. Park, K. B. Kwack, C. Chu, and H. L. Kim, "Development of a predictive model for type 2 diabetes mellitus using genetic and clinical data, " Osong Public Health Res. Perspect., vol. 2, no. 2, pp. 75-82, 2011.
17. N. H. Barakat, A. P. Bradley, and M. N. H. Barakat, "Intelligible support vector machines for diagnosis of diabetes mellitus, " IEEE Trans. Inform. Technol. Biomed., vol. 14, no. 4, pp. 1114-1120, Jul. 2010.
18. M. Marinov and I. Yoo, "Data-mining technologies for diabetes: A systematic review, " J. Diabetes Sci. Technol., vol. 5, no. 6, pp. 1549-1556, 2011.
19. K. Polat, and S. Guenes, "An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease, " Digit. Signal Process., vol. 17, no. 4, pp. 702-710, 2007.
20. A. Khan and K. Revett, "Data mining the PIMA dataset using rough set theory with a special emphasis on rule reduction, " in Proc. INMIC 8th Int. Multitopic Conf., 2004, pp. 334-339.
21. N. H. Barakat and A. P. Bradley, "Rule extraction from support vector machines: A sequential covering approach, " IEEE Trans. Knowl. Data Eng., vol. 19, no. 6, pp. 729-741, Jun. 2007.
22. N. Barakat and J. Diederich, "Eclectic rule-extraction from support vector machines, " Int. J. Comput. Intell., vol. 2, no. 1, pp. 59-62, 2005.
23. X. J. Fu, C. J.Ong, S.Keerthit, andG.G.Hung, "Extracting the knowledge embedded in support vector machines, " in Proc. IEEE Int. Conf. Neural Netw., 2004, pp. 107-112.
24. H. Núñez, C. Angulo, and A. Català, "Rule extraction from support vector machines, " in Proc. Eur. Symp. Artif. Neural Netw., 2002, pp. 291-296.
25. Y. Zhang, H. Su, T. Jia, and J. Chu, "Rule extraction from trained support vector machines, " in Proc. 9th Pacific-Asia Conf. Adv. Knowl. Discovery Data Mining, 2005, pp. 61-70.
26. G. Fung, S. Sandilya, and R. Rao, "Rule extraction from linear support vector machines, " in Proc. 11th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2005, pp. 32-40.
27. N. Barakat and J. Diederich, "Learning-based rule-extraction from support vector machines: Performance on benchmark data sets, " in Proc. 14th Int. Conf. Comput. Theory Appl., 2004, pp. 178-190.
28. D. Martens, B. Baesens, T. V. Gestel, and J. Vanthienen, "Comprehensible credit scoringmodels using rule extraction from support vectormachines, " Eur. J. Oper. Res., vol. 183, no. 3, pp. 1466-1476, 2006.
29. A. C. F. Chaves, M. M. B. R. Vellasco, and R. Tanscheit, "Fuzzy rule extraction from support vector machines, " in Proc. 5th Int. Conf. Hybrid Intell. Syst., 2005, pp. 6-9.
30. D. Martens, B. Baesens, and T.V.Gestel, "Decompositional rule extraction from support vector machines by active learning, " IEEE Trans. Knowl. Data Eng., vol. 21, no. 2, pp. 178-191, Feb. 2009.
31. M. A. H. Farquad, V. Ravi, and R. S. Bapi, "Rule extraction from support vector machine using modified active learning based approach: An application to CRM, " Knowl.-Based Intell. Inform. Eng. Syst., vol. 6276, pp. 461-470, 2010.
32. M. A. H. Farquad, V. Ravi, and R. S. Bapi, "Rule extraction using support vector machine based hybrid classifier, " in Proc. TENCON IEEE Region 10 Conf., 2008, pp. 1-6.
33. C. Cortes and V. Vapnik, "Support-vector networks, " Mach. Learning, vol. 20, no. 3, pp. 273-297, 1995.
34. C. C Chang and C. J. Lin, "LIBSVM: A library for support vector machines, " ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1-27, 2011.
35. C. Antonio, S. Jamie, and K. Ender, "Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning, " Found. Trends Comput. Vision, vol. 7, pp. 81-227, 2011.
36. L. Kuncheva and C.Whitaker, "Measures of diversity in classifier ensembles, " Mach. Learning, vol. 51, pp. 181-207, 2010.
37. S. M. Attard, A. H. Herring, E. J. Mayer-Davis, B. M. Popkin, J. B. Meigs, and P.Gordon-Larsen, "Multilevel examination of diabetes in modernising china: What elements of urbanisation are most associated with diabetes?" Diabetologia, vol. 55, no. 12, pp. 3182-3192, 2012.
38. Y Saeys, I. Inza, and P Larrañaga, "A review of feature selection techniques in bioinformatics, " Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007.
39. P. Paokanta, "Thalassemia knowledge elicitation using data engineering: PCA, pearson's chi square and machine learning, " Int. J. Comput. Theory Eng., vol. 4, no. 5, pp. 702-706, 2012.
40. U. Fayyad and K. Irani, "Multi-interval discretization of continuousvalued attributes for classification learning, " in Proc. 10th Proc. 13th Int. Joint Conf. Artif. Intell., 1993.
41. Q. Yanjun, "Random forest for bioinformatics, " in Ensemble Machine Learning. New York, NY, USA: Springer, 2012, pp. 307-323.