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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types

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DOI: http://dx.doi.org/10.15439/2016F515

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 323329 ()

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Abstract. This paper concerns automated identification of intrauterine growth restriction (IUGR) types by use of machine learning methods. The research presents a comparison of supervised and unsupervised learning covering single and hybrid classification, as well as clustering. Supervised learning techniques included bagging with Naive Bayes, kNN, C4.5 and SMO as base classifiers, random forest as a variant of bagging with a decision tree as a base classifier, boosting with NaiveBayes, SMO, kNN and C4.5 as base classifiers, and voting by all single classifiers using majority as a combination rule, as well as five single classification strategies: k-nearest neighbours (kNN), J48, NaiveBayes, random tree and sequential minimal optimization algorithm for training support vector machines. Unsupervised learning encompassed k-means and expectation-maximization algorithms. The major conclusion drawn from the study was that hybrid classifiers have demonstrated their potential ability to identify more accurately symmetrical and asymmetrical types of IUGR, whereas the unsupervised learning techniques produced the worst results.


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