Improved feature selection and classification for rheumatoid arthritis disease using weighted decision tree approach (REACT)

  • S. ShanmugamEmail author
  • J. Preethi


Rheumatoid arthritis (RA) is a major chronic autoimmune disorder which affects multiple organs and joints of human body. Disease varies in its behavior and concern such that an early prediction is a complex process with regard to time so the diagnosis is not an easy task for the physicians. The common existing methodologies employed to analyze the severity of RA are the clinical, laboratory and physical examinations. The advancement of data mining has been employed for the RA diagnosis through learning from history of datasets. To improve the efficiency and reliability of the approach, this paper presents a hybrid optimization strategy called REACT, which is based on the combination of the features of Iterative Dichotomiser 3 and Particle Swarm Optimization for feature selection and classification of RA. The effectiveness of the proposed diagnosis strategy is validated through its prediction accuracy, specificity, sensitivity, positive predictive value and negative predictive value with existing approaches.


Rheumatoid arthritis Weighted decision tree Decision support system Feature selection 



  1. 1.
    Bedran Z, Quiroz C, Rosa J, Catoggio LJ, Soriano ER (2013) Validation of a prediction rule for the diagnosis of rheumatoid arthritis in patients with recent onset undifferentiated arthritis. Int J Rheumatol 1:1. CrossRefGoogle Scholar
  2. 2.
    Garcia-Zapirain B, Garcia-Chimeno Y (2015) Machine learning techniques for automatic classification of patients with fibromyalgia and arthritis. Int J Comput Trends Technol 25(3):149–152CrossRefGoogle Scholar
  3. 3.
    Lim CK, Yew KM, Ng KH, Abdullah B (2002) A proposed hierarchical fuzzy inference system for the diagnosis of arthritic diseases. Australas Phys Eng Sci Med 25:144–150CrossRefGoogle Scholar
  4. 4.
    Cader MZ, Filer A, Hazlehurst J, de Pablo P, Buckley CD, Raza K (2011) Performance of the 2010 ACR/EULAR criteria for rheumatoid arthritis: comparison with 1987 ACR criteria in a very early synovitis cohort. Ann Rheum Dis. Google Scholar
  5. 5.
    Sangaiah AK, Thangavelu A, Sugumaran V (2017) Computational intelligence applications in business intelligence and big data analytics. Auerbach Publications, Philadelphia. ISBN 9781498761017Google Scholar
  6. 6.
    Casanova R, Saldana S, Chew EY, Danis RP, Greven CM, Ambrosius WT (2014) Application of random forests methods to diabetic retinopathy classification analyses. PLoS ONE 9(6):e98587. CrossRefGoogle Scholar
  7. 7.
    Debray S et al (1992) Weighted decision trees. In: Proceedings of the joint international conference and symposium on logic programming. MIT Press, pp 654–668Google Scholar
  8. 8.
    Jafarzadeh SR, Felson DT (2017) Updated estimates suggest a much higher prevalence of arthritis in US adults than previous ones [published online November 27, 2017]. Arthritis Rheumatol. Google Scholar
  9. 9.
    He F, Yang HM, Wang G, Cui GD (2012) A novel method for hepatitis disease diagnosis based on RS and PSO. In: Proceedings of International Conference of 4th Electronic System-Integration Technology Conference, pp 1289–1292Google Scholar
  10. 10.
    Littlestone N, Warmuth MK (1994) The weighted majority algorithm. Inform Comput 108:212–261MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Briggs FBS, Ramsay PP (2010) Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis. Genes Immun 11:199–208CrossRefGoogle Scholar
  12. 12.
    Leitich H, Adlassnig K, Kolarz G (1996) Development and evaluation of fuzzy criteria for the diagnosis of rheumatoid. Methods Inf Med 35:334–342CrossRefGoogle Scholar
  13. 13.
    Shiezadeh Z, Sajedi H, Aflakie E (2015) Diagnosis of rheumatoid arthritis using an ensemble learning approach. In: ICAITA, SAI, CDKP, Signal, pp 139–148Google Scholar
  14. 14.
    Chuang LY, Tsai SW, Yang CH (2011) Improved binary particle swarm optimization using catfish effect for feature selection. Expert Syst Appl 38:12699–12707CrossRefGoogle Scholar
  15. 15.
    Yarnold PR, Soltysik RC (2010) Maximizing accuracy of classification trees by optimal pruning. Optim Data Anal 1:10–22Google Scholar
  16. 16.
    Singh S, Kumar A, Panneerselvam K, Vennila J (2012) Diagnosis of arthritis through fuzzy inference system. J Med Syst 36:1459–1468CrossRefGoogle Scholar
  17. 17.
  18. 18.
    Toussi M, Lamy JB, Le Toumelin P, Venot A (2009) Using data mining techniques to explore physicians’ therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes. BMC Med Inform Decis Mak 9:28CrossRefGoogle Scholar
  19. 19.
    Scott IC et al (2013) Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. PLoS Genet 9(9):e1003808. CrossRefGoogle Scholar
  20. 20.
    Mohan VK, Ganesan N, Gopalakrishnan R (2014) Association of susceptible genetic markers and autoantibodies in rheumatoid arthritis. J Genet 93(2):597–605CrossRefGoogle Scholar
  21. 21.
    Feng Y, Janeja VP et al (2015) Classifying primary outcomes in rheumatoid arthritis: knowledge discovery from clinical trial metadata. IEEE Trans Inf Technol Biomed 10(2):1–2Google Scholar
  22. 22.
    Naz R, Ahmad M, Karandikar M (2015) Arthritis prediction by thermal image processing & neural network. IOSR J VLSI Signal Process 5(4):28–34Google Scholar
  23. 23.
    Louis Bridges S Jr, Kimberly RP (2010) Genetic influences on treatment response in rheumatoid arthritis. Mod Ther Rheum Dis, TotowaGoogle Scholar
  24. 24.
    Montejo L (2014) Computational methods for the diagnosis of rheumatoid arthritis with diffuse optical tomography. Doctoral Theses.
  25. 25.
    Chin CY, Weng MY, Lin TC, Cheng SY, Yang YHK, Tseng VS (2015) Mining disease risk patterns from nationwide, clinical databases for the assessment of early rheumatoid arthritis risk. PLoS ONE 10(4):e0122508CrossRefGoogle Scholar
  26. 26.
    McNally E, Keogh C, Galvin R, Fahey T (2014) Diagnostic accuracy of a clinical prediction rule (CPR) for identifying patients with recent-onset undifferentiated arthritis who are at a high risk of developing rheumatoid arthritis: a systematic review and meta-analysis. In: Seminars in Arthritis and Rheumatism, pp 498–507.
  27. 27.
    Quinlan JR (1986) Introduction of decision trees. Mach Learn 1:81. Google Scholar
  28. 28.
    Nair SS, French RM, Laroche D, Thomas E (2014) The application of machine learning algorithms to the analysis of electromyographic patterns from arthritic patients. IEEE Trans Neural Syst Rehabilit Eng 4:1–10Google Scholar
  29. 29.
    Stilou S, Bamidis PD, Maglaveras N, Pappas C (2001) Mining association rules from clinical databases: an intelligent diagnostic process in healthcare. Stud Health Technol Inform 84:1399–1403Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of CSEKongunadu College of Engineering and TechnologyThottiam Trichy DTIndia
  2. 2.Department of CSEAnna University Regional Campus, CoimbatoreCoimbatoreIndia

Personalised recommendations