使用数据挖掘检测医疗保险欺诈行为.
Zain Hamid1, Fatima Khalique1, Saba Mahmood1
1Department of Computer Science, Bahria University, Islamabad, Pakistan.
BMC medical informatics and decision making
|April 26, 2024
概括
这项研究引入了一种新的方法来检测医疗保险欺诈行为,通过结合关联规则挖掘和无监督学习. 该方法有效地识别了复杂的医疗保健数据中的欺诈模式,提高了检测准确性和效率.
科学领域:
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 医疗保险欺诈是一个重大挑战,随着复杂的计划的演变而演变.
- 由于数据问题,实时需求,隐私和标准化,检测欺诈是复杂的.
- 现有的方法与不断变化的欺诈策略和数据复杂性作斗争.
研究的目的:
- 为医疗保险提供一种新的欺诈检测方法.
- 为了利用协会规则挖掘和无监督学习来加强欺诈的识别.
- 分析拟议方法在现实世界医疗保健数据上的有效性.
主要方法:
- 利用关联规则挖掘来从医疗保健交易中提取频繁的模式.
- 应用无监督学习分类器 (IF,CBLOF,ECOD,OCSVM) 来识别异常.
- 雇佣了医疗保险和医疗补助服务中心 (CMS) 的DE-SynPUF数据集 (2008-2010).
主要成果:
- 与无监督技术相结合的协会规则挖掘比基线异常检测 (902.24s) 快 (868.18s).
- CBLOF获得了最高的轮得分 (0.114),表明异常检测效率优越.
- 描述性分析揭示了诊断,程序代码和医生之间的重要关系.
结论:
- 拟议的方法有效地提高了医疗保险欺诈的检测.
- 将模式发现 (关联规则) 与异常检测 (无监督分类器) 结合起来可以提高准确性.
- 这种方法提供了一个强大的解决方案,用于识别医疗保健中复杂的欺诈活动.
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