Assessment of Machine Learning Algorithms for the Purpose of Primary Sjögren’s Syndrome Grade Classification from Segmented Ultrasonography Images
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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.
KeywordsSjögren’s syndrome Classification Ultrasonography
This study was funded by the grants from the Serbia III41007, ON174028 and EC HORIZON2020 HarmonicSS project.
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