基于多目标进化算法的新冠肺炎数据分析因果关联规则挖掘

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Santiago Sinisterra-Sierra, Salvador Godoy-Calderón, Miriam Pescador-Rojas
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引用次数: 1

摘要

关联规则挖掘在医学领域中发现数据集属性之间的有趣关系起着至关重要的作用。传统的关联规则挖掘算法(如Apriori、FP growth或Eclat)需要大量的计算资源并生成大量的规则。此外,这些技术依赖于用户定义的阈值,这可能会无意中导致算法忽略一些有趣的规则。为了解决这些挑战,我们提出了一种基于NSGA-II的进化多目标算法,用于指导由1550万条记录组成的数据集的挖掘过程,这些数据集包含描述墨西哥COVID-19大流行的官方数据。我们测试了不同的场景,优化了四波经典和因果估计方法,定义为COVID-19患者人数增加的时间段。提议的贡献生成、重组和评估模式,重点是恢复有希望的高质量规则,这些规则具有属性之间可操作的因果关系,以确定哪些群体更容易受到疾病的影响,或者哪些条件组合是接受某些类型医疗护理所必需的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining
Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as Apriori, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care.
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来源期刊
Mathematical & Computational Applications
Mathematical & Computational Applications MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
自引率
10.50%
发文量
86
审稿时长
12 weeks
期刊介绍: Mathematical and Computational Applications (MCA) is devoted to original research in the field of engineering, natural sciences or social sciences where mathematical and/or computational techniques are necessary for solving specific problems. The aim of the journal is to provide a medium by which a wide range of experience can be exchanged among researchers from diverse fields such as engineering (electrical, mechanical, civil, industrial, aeronautical, nuclear etc.), natural sciences (physics, mathematics, chemistry, biology etc.) or social sciences (administrative sciences, economics, political sciences etc.). The papers may be theoretical where mathematics is used in a nontrivial way or computational or combination of both. Each paper submitted will be reviewed and only papers of highest quality that contain original ideas and research will be published. Papers containing only experimental techniques and abstract mathematics without any sign of application are discouraged.
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