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
  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.

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Keywords
AdaBoost, classification, Fibromyalgia, arthritis.