基于模糊决策树的医疗数据表征方法

C. Marsala
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引用次数: 13

摘要

在本文中,介绍了两个医学实验,其中使用模糊机器学习工具可以更好地了解参与研究的患者。利用模糊集理论提供模糊标签,构建模糊决策树生成模糊规则库,大大提高了可理解性,使医学家能够更好地理解患者描述与其医疗类别之间的相关性。这两个实验的结果突出了模糊数据挖掘方法在处理真实世界数据和造福社会方面的有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fuzzy decision tree based approach to characterize medical data
In this paper, two medical experiments are presented where the use of a fuzzy machine learning tool brought out a better understanding of the patients involved in the study. The use of fuzzy set theory to provide fuzzy labels and the construction of fuzzy decision trees to generate fuzzy rule bases enhance greatly the understandability and enable the Medical scientists to have a better understanding of the correlations between the description of the patients and their medical class. The results obtained in these two experiments highlight the usefulness of fuzzy data mining approach to handle real world data and to benefit Society.
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