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Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach
Mathematics ( IF 2.3 ) Pub Date : 2020-10-30 , DOI: 10.3390/math8111887 Aleksandar Aleksić , Slobodan Nedeljković , Mihailo Jovanović , Miloš Ranđelović , Marko Vuković , Vladica Stojanović , Radovan Radovanović , Milan Ranđelović , Dragan Ranđelović
Mathematics ( IF 2.3 ) Pub Date : 2020-10-30 , DOI: 10.3390/math8111887 Aleksandar Aleksić , Slobodan Nedeljković , Mihailo Jovanović , Miloš Ranđelović , Marko Vuković , Vladica Stojanović , Radovan Radovanović , Milan Ranđelović , Dragan Ranđelović
The main motivation to conduct the study presented in this paper was the fact that due to the development of improved solutions for prediction risk of bleeding and thus a faster and more accurate diagnosis of complications in cirrhotic patients, mortality of cirrhosis patients caused by bleeding of varices fell at the turn in the 21th century. Due to this fact, an additional research in this field is needed. The objective of this paper is to develop one prediction model that determines most important factors for bleeding in liver cirrhosis, which is useful for diagnosis and future treatment of patients. To achieve this goal, authors proposed one ensemble data mining methodology, as the most modern in the field of prediction, for integrating on one new way the two most commonly used techniques in prediction, classification with precede attribute number reduction and multiple logistic regression for calibration. Method was evaluated in the study, which analyzed the occurrence of variceal bleeding for 96 patients from the Clinical Center of Nis, Serbia, using 29 data from clinical to the color Doppler. Obtained results showed that proposed method with such big number and different types of data demonstrates better characteristics than individual technique integrated into it.
中文翻译:
集成数据挖掘方法预测肝硬化疾病出血的重要因素
进行本文研究的主要动机是由于开发了改进的预测出血风险的解决方案,从而更快,更准确地诊断肝硬化患者并发症,静脉曲张破裂出血所致肝硬化患者的死亡率这一事实倒在21世纪。由于这个事实,需要在该领域中进行其他研究。本文的目的是开发一种预测模型,该模型可以确定肝硬化出血的最重要因素,这对于诊断和将来治疗患者很有用。为了实现这一目标,作者提出了一种整体数据挖掘方法,作为预测领域中最先进的方法,用于以一种新的方式集成预测中最常用的两种技术,具有先验属性数量减少的分类,以及用于校准的多重逻辑回归。该研究对方法进行了评估,该方法使用来自临床到彩色多普勒的29项数据分析了来自塞尔维亚尼斯临床中心的96例静脉曲张破裂出血的发生。所得结果表明,所提出的方法具有如此众多的数据和不同类型的数据,比集成到其中的单个技术具有更好的特性。
更新日期:2020-10-30
中文翻译:
集成数据挖掘方法预测肝硬化疾病出血的重要因素
进行本文研究的主要动机是由于开发了改进的预测出血风险的解决方案,从而更快,更准确地诊断肝硬化患者并发症,静脉曲张破裂出血所致肝硬化患者的死亡率这一事实倒在21世纪。由于这个事实,需要在该领域中进行其他研究。本文的目的是开发一种预测模型,该模型可以确定肝硬化出血的最重要因素,这对于诊断和将来治疗患者很有用。为了实现这一目标,作者提出了一种整体数据挖掘方法,作为预测领域中最先进的方法,用于以一种新的方式集成预测中最常用的两种技术,具有先验属性数量减少的分类,以及用于校准的多重逻辑回归。该研究对方法进行了评估,该方法使用来自临床到彩色多普勒的29项数据分析了来自塞尔维亚尼斯临床中心的96例静脉曲张破裂出血的发生。所得结果表明,所提出的方法具有如此众多的数据和不同类型的数据,比集成到其中的单个技术具有更好的特性。