An intelligent unsupervised technique for fraud detection in health care systems
An intelligent unsupervised technique for fraud detection in health care systems
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DOI：
10.3233/idt-200052
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发表时间：
2021-01-01
影响因子：
1
通讯作者：
Khamparia, Aditya
中科院分区：
文献类型：
--
作者：
Kanksha;Bhaskar, Aman;Khamparia, Aditya
医疗保健是人们生活的重要组成部分，特别是对老年人来说，而且应该是经济的。医疗保险是一种特殊的医疗保健计划。索赔欺诈是增加医疗费用的一个重要因素，尽管欺诈检测可以减轻其影响。本文分析了各种机器学习技术来识别医疗保险欺诈。孤立森林是一种无监督的机器学习算法，它在基于离群值检测欺诈的同时提高了整体性能。这篇具体论文的目的通常是根据他们的指控显示可能不诚实的供应商。所获得的结果被认为是更有前途的现有技术相比。使用孤立森林算法获得了约98.76%的准确率。
Healthcare is an essential part of people's lives, particularly for the elderly population, and also should be economical. Medicare is one particular healthcare plan. Claims fraud is a significant contributor to increased healthcare expenses, though the effect of it could be lessened by fraud detection. In this paper, an analysis of various machine learning techniques was done to identify Medicare fraud. The isolated forest an unsupervised machine learning algorithm which improves overall performance while detecting fraud based upon outliers. The goal of this specific paper is generally to show probable dishonest providers on the ground of their allegations. Obtained results were found more promising compared to existing techniques. Around 98.76% accuracy is obtained using an isolated forest algorithm.