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Comparative Analysis for Computer-Based Decision Support: Case Study of Knee Osteoarthritis

  • Philippa Grace McCabeEmail author
  • Ivan Olier
  • Sandra Ortega-Martorell
  • Ian Jarman
  • Vasilios Baltzopoulos
  • Paulo Lisboa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)

Abstract

This case study benchmarks a range of statistical and machine learning methods relevant to computer-based decision support in clinical medicine, focusing on the diagnosis of knee osteoarthritis at first presentation. The methods, comprising logistic regression, Multilayer Perceptron (MLP), Chi-square Automatic Interaction Detector (CHAID) and Classification and Regression Trees (CART), are applied to a public domain database, the Osteoarthritis Initiative (OAI), a 10 year longitudinal study starting in 2002 (n = 4,796). In this real-world application, it is shown that logistic regression is comparable with the neural networks and decision trees for discrimination of positive diagnosis on this data set. This is likely because of weak non-linearities among high levels of noise. After comparing the explanations provided by the different methods, it is concluded that the interpretability of the risk score index provided by logistic regression is expressed in a form that most naturally integrates with clinical reasoning. The reason for this is that it gives a statistical assessment of the weight of evidence for making the diagnosis, so providing a direction for future research to improve explanation of generic non-linear models.

Keywords

Comparative analysis Neural networks Logistic regression Decision trees Osteoarthritis Healthcare 

Notes

Acknowledgement

This work was funded under EU Grant OActive from the European Community’s H2020 Programme. The OActive project looks to use advanced multi-scale computer models to better understand the risk factors associated with OA in order to prevent and delay the onset and progression of OA. Grant number 777159.

References

  1. 1.
    Vellido, A.: Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04051-w
  2. 2.
    Song, Y., Lu, Y.: Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiatry. 27, 130–135 (2015).  https://doi.org/10.11919/j.issn.1002-0829.215044CrossRefGoogle Scholar
  3. 3.
    Peat, G., Mccarney, R., Croft, P.: Knee pain and osteoarthritis in older adults: a review of community burden and current use of primary health care. Ann. Rheum. Dis. 60, 91–97 (2001).  https://doi.org/10.1136/ard.60.2.91CrossRefGoogle Scholar
  4. 4.
    Kohn, M.D., Sassoon, A.A., Fernando, N.D.: Classifications in brief: kellgren-lawrence classification of osteoarthritis. Clin. Orthop. Relat. Res. 474, 1886–1893 (2016).  https://doi.org/10.1007/s11999-016-4732-4CrossRefGoogle Scholar
  5. 5.
    Jones, L., Golan, D., Hanna, S., Ramachandran, M.: Artificial intelligence, machine learning and the evolution of healthcare. Bone Joint Res. 7, 223–225 (2017).  https://doi.org/10.1302/2046-3758.73.BJRCrossRefGoogle Scholar
  6. 6.
    Woolf, A.D., Pfleger, B.: Burden of major musculoskeletal conditions. Bull. World Health Organ. 81, 646–656 (2003).  https://doi.org/10.1590/S0042-96862003000900007CrossRefGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
    Hampton, T.: Osteoarthritis initiative. JAMA 291, 1951 (2004).  https://doi.org/10.1001/jama.291.16.1951-aCrossRefGoogle Scholar
  10. 10.
    Shipe, M.E., Deppen, S.A., Farjah, F., Grogan, E.L.: Developing prediction models for clinical use using logistic regression: an overview. J. Thorac. Dis. 11, 574–584 (2019).  https://doi.org/10.21037/jtd.2019.01.25CrossRefGoogle Scholar
  11. 11.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1 (1986)Google Scholar
  12. 12.
    Marzban, C.: The ROC Curve and the Area under It as Performance Measures (2004)CrossRefGoogle Scholar
  13. 13.
    Ing, E.B., Ing, R.: The use of a nomogram to visually interpret a logistic regression prediction model for giant cell arteritis. Neuro-Ophthalmol. 42, 284–286 (2018).  https://doi.org/10.1080/01658107.2018.1425728CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Applied MathematicsLiverpool John Moores UniversityLiverpoolUK
  2. 2.Research Institute for Sport and Exercise SciencesLiverpool John Moores UniversityLiverpoolUK

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