Assessment of Machine Learning Algorithms for the Purpose of Primary Sjögren’s Syndrome Grade Classification from Segmented Ultrasonography Images

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 241)


Primary Sjögren’s syndrome (pSS) is a chronic autoimmune disease that affects primarily women (9 females/1 male). Recently, a great interest has arisen for salivary gland ultrasonography (SGUS) as a valuable tool for the assessment of major salivary gland involvement in primary Sjögren’s syndrome. The aim of this study was to assess accuracy of state of the art machine learning algorithms for the purpose of classifying pSS from SGUS images. The five-step procedure was carried out, including: image pre- processing, feature extraction, data set balancing and feature extraction, classifiers (K-Nearest Neighbour, Decision trees, Naive bayes, Discriminant analysis classifier, Random forest, Multilayer perceptron, Linear logistic regression) learning and their corresponding assessment. The preliminary results on the growing HarmonicSS cohort showed that Naive bayes (72.8% accuracy on training set, and 73.3% accuracy on test set) and Multilayer perceptron (85.0% accuracy in training stage, and 70.1% accuracy at test stage) are the most suitable for the purpose of pSS grade classification.


Sjögren’s syndrome Classification Ultrasonography 



This study was funded by the grants from the Serbia III41007, ON174028 and EC HORIZON2020 HarmonicSS project.


  1. 1.
    Mavragani, C.P., Moutsopoulos, H.M.: Sjögren syndrome. CMAJ 186(15), E579–E586 (2014). Scholar
  2. 2.
    Shapira, Y., Agmon-Levin, N., Shoenfeld, Y.: Geoepidemiology of autoimmune rheumatic diseases. Nat. Rev. Rheumatol. 6(8), 468–476 (2010). Scholar
  3. 3.
    Ramos-Casals, M., Brito-Zerón, P., Kostov, B., Sisó-Almirall, A., Bosch, X., Buss, D., Trilla, A., Stone, J.H., Khamashta, M.A., Shoenfeld, Y.: Google-driven search for big data in autoimmune geoepidemiology: analysis of 394,827 patients with systemic autoimmune diseases. Autoimmun. Rev. 14(8), 670–679 (2015). Scholar
  4. 4.
    Baldini, C., Luciano, N., Tarantini, G., Pascale, R., Sernissi, F., Mosca, M., Caramella, D., Bombardieri, S.: Salivary gland ultrasonography: a highly specific tool for the early diagnosis of primary Sjögren’s syndrome. Arthritis Res. Ther. 17(1), 146 (2015). Scholar
  5. 5.
    Wiener, N.: Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Wiley, New York (1949). ISBN 0-262-73005-7zbMATHGoogle Scholar
  6. 6.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29, 51–59 (1996)CrossRefGoogle Scholar
  7. 7.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)zbMATHGoogle Scholar
  8. 8.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)CrossRefGoogle Scholar
  9. 9.
    Frank, E., Hall, M.A, Witten, I.H.: The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, 4th edn. Morgan Kaufmann, Massachusetts (2016)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.BioIRC, Bioengineering Research and Development CenterKragujevacSerbia
  2. 2.Faculty of EngineeringUniversity of KragujevacKragujevacSerbia
  3. 3.Azienda Ospedaliero Universitaria, Santa Maria Della Misericordia di UdineUdineItaly

Personalised recommendations