心力衰竭数据集的语言变量消除

J. Bohacik, K. Matiaško, Miroslav Benedikovic
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引用次数: 2

摘要

心力衰竭患者通常会出现致残症状。除了这些症状外,一半被诊断为心力衰竭的患者在四年内死亡。心力衰竭的患病率目前约占成年人口的2%-3%,预计还会增长。预测心力衰竭患者是否会很快死亡,从而将危及生命的情况和成本降至最低,这是一件有趣的事情。本文提出了一种基于医院现有数据的语言变量消除中发现不同真值阈值模糊规则的数据挖掘方法。通过使用模糊集、隶属函数和隶属度来考虑认知不确定性。讨论了心衰患者死亡预测的准确性和模糊规则的可解释性。我们的研究表明,与其他数据挖掘方法相比,它对这种类型的预测是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linguistic variable elimination for a heart failure dataset
Patients with heart failure often suffer disabling symptoms. In addition to these symptoms, half of all patients diagnosed with heart failure die within four years. The prevalence of heart failure is currently about 2%-3% of the adult population and it is expected to grow. It is interesting to predict if a patient with heart failure dies soon so that life-threatening situations and costs are minimized. In this paper, a data mining method for discovering fuzzy rules with different truth level thresholds in linguistic variable elimination for prediction of death on the basis of data available in hospitals is presented. Cognitive uncertainties are taken into consideration through the use of fuzzy sets, membership functions and membership degrees. The accuracy of the prediction of the death for a patient with heart failure and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other data mining methods, that it is useful for this type of prediction.
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