IJCTT - Machine Learning Techniques for Automatic Classification of Patients with Fibromyalgia and Arthritis

Machine Learning Techniques for Automatic Classification of Patients with Fibromyalgia and Arthritis

  IJCTT-book-cover
 		 International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-25 Number-3
Year of Publication : 2015
Authors : Begoña Garcia-Zapirain, Yolanda Garcia-Chimeno, Heather Rogers
DOI :  10.14445/22312803/IJCTT-V25P129

MLA

Begoña Garcia-Zapirain, Yolanda Garcia-Chimeno, Heather Rogers "Machine Learning Techniques for Automatic Classification of Patients with Fibromyalgia and Arthritis". International Journal of Computer Trends and Technology (IJCTT) V25(3):149-152, July 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The ADABoost classifier is a very powerful tool for helping to diagnose multiple diseases. With some critical features related to the pathology, the classifier can automatically perform the subjects classification. In this way, the automatic classification is a useful aid for the doctor to make the diagnosis. In this manuscript, the authors have achieved a specific classification for fibromyalgia and rheumatoid arthritis using medico-social and psychopathological features obtained from specific questionnaires. It has obtained success rate above 89%, reaching a 97.8596% in the best case. With these results, it can avoid the innumerable and uncomfortable medical tests to diagnose the pathology, saving time and money.

References
[1] K. P. Murphy, Machine learning: a probabilistic perspective. MIT press, 2012.
[2] K. Yoshida and A. Sakurai, Machine Learning. Bidgoli H Ed. Encyclopedia of Information Systems, Elsevier, New York, pp. 103-114, 2003.
[3] I. I. Hudson, M. S. Hudson, L. F. Pliner, et al. Fibromyalgia and major affective disorder: A controlled phenomenology and family history study. Am I Psychiatry, vol 142. Pp. 441- 446, 1985.
[4] G. Consoli, D. Marazziti, A. Ciapparelli, L. Bazzichi, G. Massimetti, C. Giacomelli, et al. The impact of mood, anxiety, and sleep disorders on fibromyalgia. Comprehensive Psychiatry, vol 53, pp. 962-967, 2012.
[5] (2010) Centers for Disease Control and Prevention. Available: http://www.cdc.gov/arthritis/basics/fibromyalgia.htm, http://www.cdc.gov/arthritis/basics/rheumatoid.htm
[6] F. Salaffi, P. Sarzi, P. Puttini, R. Girolimetti, F. Atzeni, S. Gasparini, et al. Health-related quality of life in fibromyalgia patients: a comparison with rheumatoid arthritis patients and the general population using the SF-36 health survey. Clinical and Experimental Rheumatology, vol. 25, pp. 67-74, 2009.
[7] D. L. Scott, F. Wolfe and T. W. Huizinga. Rheumatoid arthritis. Lancet, vol 25(376), p. 9746, 2010.
[8] (2010) American College of Rheumatology. Available: http://www.rheumatology.org/Practice/Clinical/Patients/Dise ases_And_Conditions/Rheumatoid_Arthritis/
[9] B. Sundermann, M. Burgmer, E. Pogatzki-Zahn, M. Gaubitz, C. Stüber, E. Wessolleck, et al. Diagnostic classification based on functional coneectivity in chronic pain: model optimization in fibromyalgia and rheumatoid arthritis. Academic radiology, vol. 21(3), pp. 369-377, 2014.
[10] Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In ICML, vol 96, pp. 148-156, 1996.
[11] K. P. Bennett and A. Demiriz, R. Maclin. Exploiting unlabeled data in ensemble methods. In Proceeding of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 289-296, 2002.
[12] R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, vol 2, pp. 1137-1145, 1995.
[13] L. Derogatis. SCL-90. Administration, scoring, and proceduresmanual-I for the R (revised) version and other instruments of the Psychopathology Rating Scales Series. Chicago: JohnsHopkins University School of Medicine, 1997.
[14] A. Savio, M. Garcia-Sebastian, M. Graña and J. Villanua. Results of an adaboost approach on Alzheimer’s disease detection on MRI. In Bioinspired Applications in Artificial and Natural Computation. Springer berlin Heidelberg, pp. 114-123, 2009.
[15] D. De Seny, M. Fillet, M. A. Meuwis, P. Geurts, L. Lutteri, C. Ribbens, et al. Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced lase desorption/ionization time-of-flight mass spectrometry ProteinChip approach. Arthritis & Rheumatism, vol 52(12), pp. 3801-3812, 2005.

Keywords
AdaBoost, classification, Fibromyalgia, arthritis.