Abstract:
:According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a "shrink" method, based on features selection, was introduced and applied. At the first stage of classification the results of the "shrink" case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful.
journal_name
Biomed Res Intjournal_title
BioMed research internationalauthors
Khachidze M,Tsintsadze M,Archuadze Mdoi
10.1155/2016/8313454subject
Has Abstractpub_date
2016-01-01 00:00:00pages
8313454eissn
2314-6133issn
2314-6141journal_volume
2016pub_type
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