基于电子案例数据挖掘的改进Apriori算法研究

Xiaoli Wang, Kui Su, Lirong Su
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引用次数: 3

摘要

本文对常用的Apriori算法进行了改进,提出了一种新的基于事件ID的Apriori算法。本文通过新的Apriori算法从海量医疗数据中获得关联规则。本文提出并在预测系统中应用关联规则。本文旨在使生活方式相关疾病预测系统更好地为个人、家庭和全社会服务。该预测系统在输入用户的基本信息后,可以自动给出用户的健康相关信息,并根据生成的数据给出一些有价值的建议,帮助人们实现自我决定的健康参与。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Improved Apriori Algorithm Based on Data Mining in Electronic Cases
This article makes progress of a commonly used Apriori algorithm, and proposes a new Apriori algorithm based on event ID. In this article, association rules are gained from massive medical data through the new Apriori algorithm. This article proposes and then uses the association rules in the prediction system. This article aims at making the lifestyle-related diseases prediction system provide better service for people, for families and for the whole society. The prediction system can automatically give out health-related information of the user after the person's basic information is put in, and it would also give out some pieces of valuable advice according to the resultant data, helping people realize self-determinant health engagement.
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