利用Apriori算法挖掘医疗数据识别常见疾病

M. Ilayaraja, T. Meyyappan
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引用次数: 96

摘要

数据挖掘是从不同的角度对海量数据进行分析,并将其总结为有用信息的过程。这些信息可以转化为关于历史模式和未来趋势的知识。数据挖掘在信息技术领域起着重要的作用。当今的医疗保健行业产生了大量复杂的数据,涉及患者、医院资源、疾病、诊断方法、电子病历等。数据挖掘技术对于制定治疗疾病的药物决策非常有用。医疗保健行业收集了大量的医疗保健数据,不幸的是,这些数据没有被“挖掘”,以发现隐藏的信息,从而进行有效的决策。医疗保健管理员可以使用发现的知识来提高服务质量。本文提出了一种基于关联规则的Apriori数据挖掘技术来识别特定地理区域在给定时间段内的疾病频率的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining medical data to identify frequent diseases using Apriori algorithm
The data mining is a process of analyzing a huge data from different perspectives and summarizing it into useful information. The information can be converted into knowledge about historical patterns and future trends. Data mining plays a significant role in the field of information technology. Health care industry today generates large amounts of complex data about patients, hospitals resources, diseases, diagnosis methods, electronic patients records, etc,. The data mining techniques are very useful to make medicinal decisions in curing diseases. The healthcare industry collects huge amount of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. The discovered knowledge can be used by the healthcare administrators to improve the quality of service. In this paper, authors developed a method to identify frequency of diseases in particular geographical area at given time period with the aid of association rule based Apriori data mining technique.
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