Heart Disease Prediction using Evolutionary Rule Learning

A. Chauhan, Aditya Jain, Purushottam Sharma, V. Deep
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引用次数: 39

Abstract

In modern society, Heart disease is the noteworthy reason for short life. Large population of people depends on the healthcare system so that they can get accurate result in less time. Large amount of data is produced and collected by the healthcare organization on the daily basis. To get intriguing knowledge, data innovation permits to extract the data through automization of processes. Weighted Association Rule is a type of data mining technique used to eliminate the manual task which also helps in extracting the data directly from the electronic records. This will help in decreasing the cost of services and also helps in saving lives. In this paper, we will find the rule to predict patient's risk of having coronary disease. Test results have shown that vast majority of the rules helps in the best prediction of coronary illness.
利用进化规则学习进行心脏病预测
在现代社会,心脏病是缩短寿命的重要原因。大量人口依赖于医疗保健系统,以便他们能够在更短的时间内获得准确的结果。医疗保健组织每天都会产生和收集大量数据。为了获得有趣的知识,数据创新允许通过流程自动化提取数据。加权关联规则是一种用于消除人工任务的数据挖掘技术,它也有助于直接从电子记录中提取数据。这将有助于降低服务成本,也有助于挽救生命。在本文中,我们将找到预测患者患冠心病风险的规律。测试结果表明,绝大多数规则有助于最好地预测冠状动脉疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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