Performance on Fraud Detection in Medical Claims of Healthcare Data
P.Naga Jyothi1, D Rajya lakshmi2, K.V.S.N.Rama Rao3

1P.Naga Jyothi, Research Scholar, Department of CSE, K L Educational Foundation, Guntur (Andhra Pradesh), India.
2D Rajya Lakshmi, Professor, Department of Computer Science and Engineering, JNTUK UCEV, Narasaroa Peta (Andhra Pradesh), India.
3K.V.S.N.Rama Rao, Professor, Dept. of CSE, K L Educational Foundation, Guntur (Andhra Pradesh), India.

Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 1158-1165 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6348058719/19©BEIESP
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Abstract: Healthcare is more focused by most of the individuals. As the expenditure margin is beyond their limits too, the people do not care much about, apart from healthy livelihood. The interesting part of everyone’s life is the government is providing policies and the individual is also holding private policies for their medical healthcare. At the same time, the fraud is growing faster in this scenario. Detection and prevention of fraudulent activities have been increasing with various sophisticated tools. But, still there are some lapses in analyzing and finding suspicious activities and mismanagement of system in medical insurance. In this paper, we described the survey of various technologies, methods applied to medical healthcare fraud detection of an individual, corporate hospitals and industries. The survey includes various characteristics of data, what are key steps for processing and analyzing the data for classification and finding of communities’ methods for further fraud prevention and detection techniques. The majority of reviews the authors have concluded that the use of advanced machine learning techniques will improve the quality of healthcare systems. These algorithms can address some potential problems, comparisons and results were substantial with their limitations.
Keyword: Medical Healthcare, Machine Learning, Fraud Detection, Fraud Prevention Communities Suspicious Activities, Classification.
Scope of the Article: Data Mining.