Identifying risk factors for adverse diseases using dynamic rare association rule mining
Identifying risk factors for adverse diseases using dynamic rare association rule mining
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DOI：
10.1016/j.eswa.2018.07.010
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发表时间：
2018-12-15
影响因子：
8.5
通讯作者：
Nath, Bhabesh
中科院分区：
文献类型：
--
作者：
Borah, Anindita;Nath, Bhabesh
由于危及生命的疾病导致的死亡率上升已成为当今世界关注的问题。因此，有必要对疾病进行早期检测和诊断，以减少其副作用的严重程度。稀有关联规则挖掘等计算智能技术可以广泛用于疾病分析。本文介绍了一种有效的技术，以确定三个不良疾病的症状和危险因素：心血管疾病，肝炎和乳腺癌，在罕见的关联规则。稀有关联规则挖掘的现有研究是基于这样的概念，即在挖掘过程开始时，要操作的整个数据都是可用的。在实践中，由于添加新记录或删除先前记录，医疗数据库可能会随着时间的推移而被修改。此外，当数据库被更新时，用户可以切换到新的阈值以用于生成期望的稀有关联规则集。生成当前稀有关联规则集的一个简单但不称职的解决方案是从头开始重新执行整个挖掘算法，每次修改一堆数据和更新阈值。在这项研究中提出的算法是能够生成一组新的罕见的关联规则更新的医疗数据库中，在一个单一的数据库扫描，而无需重新执行整个挖掘过程。该算法能够有效地处理事务插入和删除的情况，并在阈值更新时为用户生成新的稀有关联规则集提供了灵活性。实验分析表明，该方法相对于传统的重复挖掘整个更新数据库的方法具有重要意义。(C)2018爱思唯尔有限公司版权所有
The increase in mortality rate due to life-threatening diseases has become an issue of concern in today's world. Early detection and diagnosis of diseases thus becomes necessary to reduce the severity of their side effects. Computational intelligence techniques like rare association rule mining can be extensively used for the analysis of diseases. This paper introduces an efficient technique to identify the symptoms and risk factors for three adverse diseases: cardiovascular disease, hepatitis and breast cancer, in terms of rare association rules. Existing research on rare association rule mining is based on the notion that the entire data to be operated on is available at the onset of the mining process. The medical databases in practice may get modified over time due to the addition of new records or deletion of previous records. Moreover, the user may switch to a new threshold for generating the desired set of rare association rules when the database gets updated. A straightforward yet incompetent solution for generating the current set of rare association rules would be to re-execute the entire mining algorithm from scratch, for each modified bunch of data and updated threshold. The algorithm proposed in this study is capable of generating the new set of rare association rules from updated medical databases in a single database scan without re-executing the entire mining process. It can efficiently handle the cases of transaction insertion and deletion and also provides flexibility to the user to generate the new set of rare association rules when threshold is updated. Experimental analysis illustrates the significance of proposed approach over traditional approach of repeatedly mining the entire updated database. (C) 2018 Elsevier Ltd. All rights reserved.