Applying Data Mining techniques when making medical diagnostic decisions / E. E. Mokina, O. V. Marukhina, M. D. Shagarova
Уровень набора: (RuTPU)RU\TPU\network\18167, Advances in Computer Science ResearchЯзык: английский.Страна: France.Резюме или реферат: 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..Примечания о наличии в документе библиографии/указателя: [References: p. 277 (5 tit.)].Тематика: электронный ресурс | труды учёных ТПУ | Data Mining | information systems | decision support system | SF-36 | HADS_T | интеллектуальный анализ | данные | информационные системы | системы поддержки принятия решений Ресурсы он-лайн:Щелкните здесь для доступа в онлайнTitle screen
[References: p. 277 (5 tit.)]
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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