Applying Data Mining techniques when making medical diagnostic decisions
| Parent link: | Advances in Computer Science Research Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016).— 2016.— [P. 274-277] |
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| Other Authors: | , |
| Summary: | Title screen Under the present-time conditions of the increased pace of life in large cities neurological disorders are tending to increase. The present paper considers the application of Data Mining techniques for studying medical data and building the decision support system on the basis of research results being, in the present case, the detection of the neurological disorders by the result indicators of the surveys on living standard, anxiety and depression. Throughout the use of Data Mining techniques there was built a decision tree and were established the reasoning rules, which provided the basis for the decision support system. The paper presents the basic requirements for this system enabling to reduce time of the clinical staff spent on processing survey data and providing recommendations on establishing diagnoses. |
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2016
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| Online Access: | http://dx.doi.org/10.2991/itsmssm-16.2016.48 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=654315 |
| Summary: | Title screen Under the present-time conditions of the increased pace of life in large cities neurological disorders are tending to increase. The present paper considers the application of Data Mining techniques for studying medical data and building the decision support system on the basis of research results being, in the present case, the detection of the neurological disorders by the result indicators of the surveys on living standard, anxiety and depression. Throughout the use of Data Mining techniques there was built a decision tree and were established the reasoning rules, which provided the basis for the decision support system. The paper presents the basic requirements for this system enabling to reduce time of the clinical staff spent on processing survey data and providing recommendations on establishing diagnoses. |
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| DOI: | 10.2991/itsmssm-16.2016.48 |