Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2018 (2018), Article ID 6154025, 9 pages
https://doi.org/10.1155/2018/6154025
Research Article

Prediction of Pathological Subjects Using Genetic Algorithms

Department of Mathematics, Yildiz Technical University, Esenler, Istanbul 34220, Turkey

Correspondence should be addressed to Murat Sari; rt.ude.zidliy@miras

Received 29 August 2017; Revised 13 December 2017; Accepted 2 January 2018; Published 29 January 2018

Academic Editor: Thierry Busso

Copyright © 2018 Murat Sari and Can Tuna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. C. Küpper, B. Loitz-Ramage, D. T. Corr, D. A. Hart, and J. L. Ronsky, “Measuring knee joint laxity: a review of applicable models and the need for new approaches to minimize variability,” Clinical Biomechanics, vol. 22, no. 1, pp. 1–13, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Hamill, B. T. Bates, and K. G. Holt, “Timing of lower extremity joint actions during treadmill running,” Medicine & Science in Sports & Exercise, vol. 24, no. 7, pp. 807–813, 1992. View at Google Scholar · View at Scopus
  3. S. F. Dye, “An evolutionary perspective of the knee,” The Journal of Bone & Joint Surgery, vol. 69, no. 7, pp. 976–983, 1987. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Sari and B. G. Cetiner, “Predicting effect of physical factors on tibial motion using artificial neural networks,” Expert Systems with Applications, vol. 36, no. 6, pp. 9743–9746, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. O. S. Mills and M. L. Hull, “Rotational flexibility of the human knee due to varus/valgus and axial moments in vivo,” Journal of Biomechanics, vol. 24, no. 8, pp. 673–690, 1991. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Martelli and V. Pinskerova, “The shapes of the tibial and femoral articular surfaces in relation to tibiofemoral movement,” The Journal of Bone & Joint Surgery, vol. 84, no. 4, pp. 607–613, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. K. E. Moglo and A. Shirazi-Adl, “Biomechanics of passive knee joint in drawer: Load transmission in intact and ACL-deficient joints,” The Knee, vol. 10, no. 3, pp. 265–276, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Armour, L. Forwell, R. Litchfield, A. Kirkley, N. Amendola, and P. J. Fowler, “Isokinetic evaluation of internal/external tibial rotation strength after the use of hamstring tendons for anterior cruciate ligament reconstruction,” The American Journal of Sports Medicine, vol. 32, no. 7, pp. 1639–1643, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. P. Johal, A. Williams, P. Wragg, D. Hunt, and W. Gedroyc, “Tibio-femoral movement in the living knee. A study of weight bearing and non-weight bearing knee kinematics using ‘interventional’ MRI,” Journal of Biomechanics, vol. 38, no. 2, pp. 269–276, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. L. R. Osternig, B. T. Bates, and S. L. James, “Patterns of tibial rotary torque in knees of healthy subjects,” Medicine & Science in Sports & Exercise, vol. 12, no. 3, pp. 195–199, 1980. View at Google Scholar · View at Scopus
  11. D. Tiberio, “The effect of excessive subtalar joint pronation on patellofemoral mechanics: a theoretical model,” Journal of Orthopaedic and Sports Physical Therapy, vol. 9, no. 4, pp. 160–165, 1987. View at Publisher · View at Google Scholar
  12. S. L. James and D. C. Jones, “Biomechanical aspects of distance running injuries,” Biomechanics of Distance Running, vol. 1, pp. 249–265, 1990. View at Google Scholar
  13. G. Li, J. Suggs, and T. Gill, “The effect of anterior cruciate ligament injury on knee joint function under a simulated muscle load: A three-dimensional computational simulation,” Annals of Biomedical Engineering, vol. 30, no. 5, pp. 713–720, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. B. G. Cetiner and M. Sari, “Tibial rotation assessment using artificial neural networks,” Mathematical and Computational Applications, vol. 15, no. 1, pp. 34–44, 2010. View at Google Scholar
  15. J. E. Bates, C. A. Freeland, and M. L. Lounsbury, “Measurement of infant difficultness.,” Child Development, vol. 50, no. 3, pp. 794–803, 1979. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Z. Herold and C. Marcovich, “Tibial torsion in untreated congenital clubfoot,” Acta Orthopaedica, vol. 47, no. 1, pp. 112–117, 1976. View at Publisher · View at Google Scholar · View at Scopus
  17. B.-G. Clementz and A. Magnusson, “Assessment of tibial torsion employing fluoroscopy, computed tomography and the cryosectioning technique,” Acta Radiologica, vol. 30, no. 1, pp. 75–80, 1989. View at Publisher · View at Google Scholar · View at Scopus
  18. P. A. Butler-Manuel, R. L. Guy, and F. W. Heatley, “Measurement of tibial torsion—a new technique applicable to ultrasound and computed tomography,” British Journal of Radiology, vol. 65, no. 770, pp. 119–126, 1992. View at Publisher · View at Google Scholar
  19. J. C. Cameron and S. Saha, “External tibial torsion: An underrecognized cause of recurrent patellar dislocation,” Clinical Orthopaedics and Related Research, no. 328, pp. 177–184, 1996. View at Publisher · View at Google Scholar · View at Scopus
  20. L. M. G. Lang and R. G. Volpe, “Measurement of tibial torsion,” Journal of the American Podiatric Medical Association, vol. 88, no. 4, pp. 160–165, 1998. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Peter, M. Schwarz, C. Conradt et al., “Heparin inhibits ligand binding to the leukocyte integrin Mac-1 (CD11b/CD18),” Circulation, vol. 100, no. 14, pp. 1533–1539, 1999. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Nakagawa, Y. Kadoya, S. Todo et al., “Tibiofemoral movement 3: full flexion in the living knee studied by MRI,” The Journal of Bone & Joint Surgery, vol. 82, no. 8, pp. 1199-1200, 2000. View at Publisher · View at Google Scholar · View at Scopus
  23. R.-Q. Sun, Y.-J. Tu, N. B. Lawand, J.-Y. Yan, Q. Lin, and W. D. Willis, “Calcitonin gene-related peptide receptor activation produces PKA- and PKC-dependent mechanical hyperalgesia and central sensitization,” Journal of Neurophysiology, vol. 92, no. 5, pp. 2859–2866, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Lin, G. Wang, J. L. Koh, R. W. Hendrix, and L.-Q. Zhang, “In vivo and Noninvasive Three-Dimensional Patellar Tracking Induced by Individual Heads of Quadriceps,” Medicine & Science in Sports & Exercise, vol. 36, no. 1, pp. 93–101, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Liu, W. Kim, B. Drerup, and A. Mahadev, “Tibial torsion measurement by surface curvature,” Clinical Biomechanics, vol. 20, no. 4, pp. 443–450, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. K. Tamari, P. Tinley, K. Briffa, and S. Raine, “A new concept of indexing tibiofibular torsion: A pilot study using dry bones,” Journal of the American Podiatric Medical Association, vol. 95, no. 5, pp. 481–485, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Sari, “Relationship between physical factors and tibial motion in healthy subjects: 2D and 3D analyses,” Advances in Therapy, vol. 24, no. 4, pp. 772–783, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Cimbiz, U. Cavlak, M. Sari, H. Hallaceli, and F. Beydemir, “A new clinical design measuring the vertical axial rotation through tibial shaft resulting from passive knee and subtalar joints rotation in healthy subjects: A reliability study,” Journal of Medical Sciences, vol. 6, no. 5, pp. 751–757, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. K. O. Jones, “Comparison of genetic algorithm and particle swarm optimization,” in Proceedings of the International Conference on Computer Systems and Technologies (CompSysTech '05), 2005.
  30. S. Panda and N. P. Padhy, “Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design,” Applied Soft Computing, vol. 8, no. 4, pp. 1418–1427, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. C.-C. Chiu, Y.-T. Cheng, and C.-W. Chang, “Comparison of particle swarm optimization and genetic algorithm for the path loss reduction in an urban area,” International Journal of Applied Science and Engineering Research, vol. 15, no. 4, pp. 371–380, 2012. View at Google Scholar · View at Scopus
  32. R. Rajendra and D. K. Pratihar, “Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot,” Intelligent Control and Automation, vol. 02, no. 04, pp. 430–449, 2011. View at Publisher · View at Google Scholar
  33. R. P. Jakob, M. Haertel, and E. Stussi, “Tibial torsion calculated by computerised tomography and compared to other methods of measurement,” The Journal of Bone & Joint Surgery, vol. 62, no. 2, pp. 238–242, 1980. View at Google Scholar · View at Scopus
  34. G. Fabry, “Normal and abnormal torsional development of the lower extremities,” Acta Orthopædica Belgica, vol. 63, no. 4, pp. 229–232, 1997. View at Google Scholar · View at Scopus
  35. D. E. Goldberg and J. H. Holland, “Genetic algorithms and machine learning,” Machine Learning, vol. 3, no. 2-3, pp. 95–99, 1998. View at Publisher · View at Google Scholar
  36. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, New York, NY, USA, 1989.
  37. J. R. Koza, Genetic Programming: on the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, Mass, USA, 1992. View at Publisher · View at Google Scholar · View at Scopus
  38. D. X. Chang, X. D. Zhang, and C. W. Zheng, “A genetic algorithm with gene rearrangement for K-means clustering,” Pattern Recognition, vol. 42, no. 7, pp. 1210–1222, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. P. Kudova, “Clustering genetic algorithm,” in Proceedings of the DEXA 2007 18th International Workshop on Database and Expert Systems Applications, pp. 138–142, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  40. U. Maulik and S. Bandyopadhyay, “Genetic algorithm-based clustering technique,” Pattern Recognition, vol. 33, no. 9, pp. 1455–1465, 2000. View at Publisher · View at Google Scholar · View at Scopus
  41. S. Bandyopadhyay and U. Maulik, “Genetic clustering for automatic evolution of clusters and application to image classification,” Pattern Recognition, vol. 35, no. 6, pp. 1197–1208, 2002. View at Publisher · View at Google Scholar · View at Scopus
  42. M. Kumar, M. Husian, N. Upreti, and D. Gupta, “Genetic algorithm: Review and application,” International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 451–454, 2010. View at Google Scholar
  43. Q. Li, H. Chen, H. Huang et al., “An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis,” Computational and Mathematical Methods in Medicine, vol. 2017, Article ID 9512741, 15 pages, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  44. S. Zong, G. Chai, and Y. Su, “Determining optimal replacement policy with an availability constraint via genetic algorithms,” Mathematical Problems in Engineering, vol. 2017, Article ID 8763101, 8 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  45. J.-H. Seo, Y. H. Lee, and Y.-H. Kim, “Feature selection for very short-term heavy rainfall prediction using evolutionary computation,” Advances in Meteorology, vol. 2014, Article ID 203545, 15 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. A. Said, R. A. Abbasi, O. Maqbool, A. Daud, and N. R. Aljohani, “CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks,” Applied Soft Computing, vol. 63, pp. 59–70, 2018. View at Publisher · View at Google Scholar
  47. L. Poli, G. Oliveri, and A. Massa, “An integer genetic algorithm for optimal clustering in phased array antenna,” in Proceedings of the 2017 International Applied Computational Electromagnetics Society Symposium - Italy (ACES), pp. 1-2, Florence, March 2017. View at Publisher · View at Google Scholar
  48. S. Das, S. Chaudhuri, and A. K. Das, “Optimal set of overlapping clusters using multi-objective genetic algorithm,” in Proceedings of the 9th International Conference on Machine Learning and Computing (ICMLC '17), pp. 232–237, February 2017. View at Publisher · View at Google Scholar · View at Scopus
  49. A. A. F. Saldivar, C. Goh, Y. Li, Y. Chen, and H. Yu, “Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm,” in Proceedings of the 22nd International Conference on Automation and Computing (ICAC '16), pp. 408–414, September 2016. View at Publisher · View at Google Scholar · View at Scopus
  50. D. Hendricks, T. Gebbie, and D. Wilcox, “High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm,” South African Journal of Science, vol. 112, no. 1-2, 2016. View at Publisher · View at Google Scholar · View at Scopus
  51. Y. Ding and X. Fu, “Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm,” Neurocomputing, vol. 188, pp. 233–238, 2016. View at Publisher · View at Google Scholar · View at Scopus
  52. J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, vol. 1, pp. 281–297, 1967.
  53. A. Alguwaizani, “Degeneracy on K-means clustering,” Electronic Notes in Discrete Mathematics, vol. 39, pp. 13–20, 2012. View at Publisher · View at Google Scholar · View at Scopus
  54. S. Das, A. Abraham, and A. Konar, “Automatic clustering using an improved differential evolution algorithm,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 38, no. 1, pp. 218–237, 2008. View at Publisher · View at Google Scholar · View at Scopus