Abstract
Classification of degenerations prevalent in human population is considered to be a crucial task which is performed by a physician or the radiologist. With numerous data being generated and innumerable features getting extracted, identification of normal and pathological case becomes a daunting process. Data learning techniques provide valuable resources in automating the entire procedure easing the burden on the consultant physician. However, since the inception of various machine learning techniques, feasible solution at the cost of computational expense needs to be evaluated. Factors considered for classification play a significant role in defining the accuracy of a system. The current study aims at demonstrating the trade off achieved at the expense of accuracy amongst the number of features and instances. In this article, vertebral column dataset from UCI repository is used for training and testing. Effect of various data pre-processing techniques are presented alongside an extensive study on feature selection method. For validation, breast tissue dataset from the former repository is considered and analyzed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shankar, H., Scarlett, J.A., Abram, S.E.: Anatomy and pathophysiology of intervertebral disc disease. Tech. Reg. Anesth. Pain Manag. 13, 67–75 (2009)
Rustagi, T., Lavelle, W.F., Tallarico, R.A.: Surgical management of adult spondylolysis and spondylolisthesis. Semin. Spine Surg. 26, 238–246 (2014)
Da Rocha Neto, A.R., Sousa, R., De A. Barreto, G., Cardoso, J.S.: Diagnostic of pathology on the vertebral column with embedded reject option. Lecture Notes Computer Science (including Subseries Lecture Notes Artificial Intelligence, Lecture Notes Bioinformatics), vol. 6669, pp. 588–595 (2011)
Schaffer, C.: Selecting a classification method by cross-validation. Mach. Learn. 13, 135–143 (1993)
Unal, Y., Polat, K., Erdinc Kocer, H.: Pairwise FCM based feature weighting for improved classification of vertebral column disorders. Comput. Biol. Med. 46, 61–70 (2014)
Unal, Y., Polat, K., Kocer, H.E.: Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering. Meas. J. Int. Meas. Confed. 77, 278–291 (2016)
Da Rocha Neto, A.R.: On the application of ensembles of classifiers to the diagnosis of pathologies of the vertebral column: A comparative analysis. IEEE Lat. Am. Trans. 7, 487–496 (2009)
Bah, T.J.: The role of data reduction for diagnosis of pathologies of the vertebral column by using supervised learning algorithms. In: 2015 XVIII International Conference on Soft Computing and Measurements (SCM), St. Petersburg, pp. 163–166 (2015)
Wasikowski, M., Chen, X., Member, S.: Combating the small sample class imbalance problem using feature selection. IEEE Trans. Knowl. Data Eng. 22, 1388–1400 (2010)
Prasetio, R.T., Riana, D.: A comparison of classification methods in vertebral column disorder with the application of genetic algorithm and bagging. In: 2015 4th International Conference on Instrumentation, Communications Information Technology and Biomedical Engineering, pp. 163–168 (2015)
Ansari, S., Sajjad, F.: Diagnosis of vertebral column disorders using machine learning classifiers. In: 2013 International Conference on Information Science and Applications (ICISA), Suwon pp. 1–6 (2013)
Frighetto-Pereira, L., Menezes-Reis, R., Metzner, G.A., Rangayyan, R.M., Azevedo-Marques, P.M., Nogueira-Barbosa, M.H.: Semiautomatic classification of benign versus malignant vertebral compression fractures using texture and gray-level features in magnetic resonance images. In: 2015 Proceedings of the - IEEE Symposium on Computer Medical Systems, pp. 88–92 (2015)
Frighetto-Pereira, L., Menezes-Reis, R., Metzner, G.A., Rangayyan, R.M., Azevedo-Marques, P.M., Nogueira-Barbosa, M.H.: Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images. Comput. Biol. Med. 73, 147–156 (2016)
Betul, A., Istanbul, O., Universitesi, M.: Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images. Comput. Med., Imaging Graph (2014)
Dong, X., Zheng, G.: Spinal Imaging and Image Analysis. Springer, Heidelberg (2015)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison - Wesley Publishing Inc., Boston (1989)
Witten, I.H., Frank, E., Hall, M.A.: Data mining - Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2011)
Acknowledgement
The first author would like to thank the Department of Science and Technology [DST], India, for supporting her research through INSPIRE fellowship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Athertya, J.S., Saravana Kumar, G. (2018). Sensitivity Analysis on Effect of Biomechanical Factors for Classifying Vertebral Deformities. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-60618-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60617-0
Online ISBN: 978-3-319-60618-7
eBook Packages: EngineeringEngineering (R0)