Ischemic heart disease detection using selected machine learning methods

References

• Allender , S. , Scarborough , P. , Peto , V. , Rayner , M. , Leal , J. , Luengo-Fernandez , R. and Gray , A. 2008 . European Cardiovascular Disease Statistics , 3 , Brussels : A European Heart Network .
   Google Scholar
• Allender , S. 2008 . European Cardiovascular Disease Statistics 2008 Edition , European Heart Network .
   Google Scholar
• Burges , C. J.C. 1998 . A tutorial on support vector machines for pattern recognition . Data Min. Knowl. Discov. , 2 ( 2 ) : 955 – 974 . (doi:10.1023/A:1009715923555)
   Web of Science ®Google Scholar
• Burges , C. J.C. and Crisp , D. 1999 . “ Uniqueness of the SVM Solution ” . In Advances in Neural Information Processing Systems , Vol. 12 , 223 – 229 . Cambridge , MA : MIT Press .
   Google Scholar
• Ciecholewski , M. 2011 . Support vector machine approach to cardiac SPECT diagnosis . LNCS , 6636 : 432 – 443 .
   Google Scholar
• Cios , K. J. , Pedrycz , W. and Swiniarski , R. 1998 . Data Mining Methods for Knowledge Discovery , Dordrecht : Kluwer Academic Publishers .
   Google Scholar
• Clark , P. and Niblett , T. 1989 . The CN2 algorithm . Mach. Learn. , 3 : 261 – 283 .
   Google Scholar
• Cristianini , N. and Shawe-Taylor , J. 2003 . An Introduction to Support Vector Machines and Other Kernel–Based Learning Methods , Cambridge : Cambridge University Press .
   Google Scholar
• Frank , A. and Asuncion , A. 2010 . UCI Machine Learning Repository , Irvine , CA : School of Information and Computer Science, University of California . Available at http://archive.ics.uci.edu/ml.
   Google Scholar
• Fung , G. and Stoeckel , J. 2007 . SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information . Knowl. Inf. Syst. , 11 ( 2 ) : 243 – 258 . (doi:10.1007/s10115-006-0043-5)
   Web of Science ®Google Scholar
• Gunn , S. 1998 . Support vector machines for classification and regression , Southampton : Tech. Rep., Department of Electronics and Computer Science, University of Southampton .
   Google Scholar
• Hastie , T. , Tibshirani , R. and Friedman , J. H. 2001 . Elements of Statistical Learning , 1 , New York : Springer-Verlag .
   Google Scholar
• Haykin , S. 1999 . Neural Networks a Comprehensive Foundation , 2 , Upper Saddle River , , NJ, USA : Prentice-Hall .
   Google Scholar
• Lloyd-Jones , D. , Adams , R. , Carnethon , M. , De Simone , G. , Ferguson , T. B. , Flegal , K. , Ford , E. , Furie , K. , Go , A. , Greenlund , K. , Haase , N. , Hailpern , S. , Ho , M. , Howard , V. , Kissela , B. , Kittner , S. , Lackland , D. , Lisabeth , L. , Marelli , A. , McDermott , M. , Meigs , J. , Mozaffarian , D. , Nichol , G. , O'Donnell , Ch. , Roger , V. , Rosamond , W. , Sacco , R. , Sorlie , P. , Stafford , R. , Steinberger , J. , Thom , T. , Wasserthiel-Smoller , S. , Wong , N. , Wylie-Rosett , J. and Hong , Y. 2009 . Heart disease and stroke statistics–2009 update: A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee . Circulation , 119 ( 3 ) : e21 – e181 . Available at http://circ.ahajournals.org/content/119/3/e21.full.pdf+html. (doi:10.1161/CIRCULATIONAHA.108.191261)
   PubMed Web of Science ®Google Scholar
• Jolliffe , I. T. 2002 . Principal Component Analysis , 2 , New-York : Springer-Verlag .
   Google Scholar
• Kalatzis , I. , Pappas , D. , Piliouras , N. and Cavouras , D. 2003 . Support vector machines based analysis of brain spect images for determining cerebral abnormalities in asymptomatic diabetic patients . Med. Inform. Internet Med. , 28 ( 3 ) : 221 – 230 . (doi:10.1080/14639230310001613449)
   PubMed Web of Science ®Google Scholar
• Karush , W. 1939 . Minima of Functions of Several Variables with Inequalities as Side Constraints Chicago , Illinois M.Sc. Dissertation, Dept. of Mathematics, Univ. of Chicago
   Google Scholar
• Kuhn , H. W. and Tucker , A. W. Nonlinear Programming . Proceedings of 2nd Berkeley Symposium . pp. 481 – 492 . Berkeley : University of California Press .
   Google Scholar
• Kurgan , L. A. , Cios , K. J. , Tadeusiewicz , R. , Ogiela , M. and Goodenday , L. S. 2001 . Knowledge discovery approach to automated cardiac SPECT diagnosis . Artif. Intell. Med. , 23 ( 2 ) : 149 – 169 . (doi:10.1016/S0933-3657(01)00082-3)
   PubMed Web of Science ®Google Scholar
• Kwon , S. S. , Shim , E. B. and Leem , Ch. H. 2007 . Computational analysis of the effects of bundle branch blocks in human heart using cardiac cell models . IFMBE Proc. , 14 ( Part 2 ) : 102 – 105 . (doi:10.1007/978-3-540-36841-0_33)
   Google Scholar
• Matías , J. M. , Ordóñez Galán , C. , Taboada , J. and Rivas , T. 2009 . Functional support vector machines and generalized linear models for glacier geomorphology analysis . Int. J. Comput. Math. , 86 ( 2 ) : 275 – 285 . (doi:10.1080/00207160801965305)
   Web of Science ®Google Scholar
• Nash , M. P. and Hunter , P. J. 2000 . Computational mechanics of the heart . J. Elasticity , 61 ( 1–3 ) : 113 – 141 . (doi:10.1023/A:1011084330767)
   Web of Science ®Google Scholar
• Osuna , E. , Freund , R. and Girosi , F. 1998 . “ An improved training algorithm for support vector machines ” . In Neural Networks for Signal Processing VII , Edited by: Principe , J. , Gile , L. , Morgan , N. and Wilson , E. 276 – 285 . New York : IEEE .
   Google Scholar
• Papa , J. P. , Falcão , A. X. , de Albuquerque , V. C. and Tavares , J. M.R.S. 2012 . Efficient supervised optimum-path forest classification for large datasets . Pattern Recognit. , 45 ( 1 ) : 512 – 520 . (doi:10.1016/j.patcog.2011.07.013)
   Web of Science ®Google Scholar
• Platt , J. 1999 . “ Fast training of support vector machines using sequential minimal optimization ” . In Advances in Kernel Methods – Support Vector Learning , Edited by: Schölkopf , B. , Burges , C. J. and Smola , A. J. 185 – 220 . Cambridge , MA : MIT Press .
   Google Scholar
• Quinlan , J. R. 1993 . C4.5: Programs for Machine Learning , San Francisco , CA : Morgan Kaufmann Publishers Inc .
   Google Scholar
• Rojas , R. 1996 . Neural Networks , New York : Springer-Verlag .
   Google Scholar
• Salas-Gonzalez , D. , Górriz , J. M. , Ramírez , J. , Segovia , F. , Chaves , R. , López , M. , Illán , I. A. and Padilla , P. 2010 . Selecting regions of interest in SPECT images using Wilcoxon test for the diagnosis of Alzheimer's disease . LNCS , 6076 : 446 – 451 . Available at http://link.springer.com/chapter/10.1007%2F978-3-642-13769-3_54.
   Google Scholar
• Santos , I. C.T. , Sepulveda , A. T. , Viana , J. C. , Pontes , A. J. , Wardle , B. L. , Sampaio , S. M. , Roncon-Albuquerque , R. , Tavares , J. M.R.S. and Rocha , L. A. 2012 . Improving Post-EVAR Surveillance with a Smart Stent-Graft, Technologies for Medical Sciences , Vol. 1 , 267 – 289 . Dordrecht : Springer Science+Business Media B.V . Lecture Notes in Computational Vision and Biomechanics
   Google Scholar
• Turk , M. and Pentland , A. Face Recognition using Eigenfaces . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . pp. 586 – 591 . Maui, Hawaii , , USA
   Google Scholar
• Vapnik , V. 1995 . The Nature of Statistical Learning Theory , New York : Springer .
   Google Scholar
• Vapnik , V. 1998 . Statistical Learning Theory , New York : John Wiley & Sons, Inc .
   Google Scholar
• Wahba , G. 1990 . Spline Models for Observational Data , Series in Applied Mathematics Vol. 59 , Philadelphia , PA : SIAM .
   Google Scholar