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ABSTRACT
The healthcare industry is a complex system and it is expanding at a rapid pace.
At the same time fraud in this industry is turning into a critical problem. One of
the issues is the misuse of the medical insurance systems. Machine learning
and data mining techniques are used for automatically detecting the healthcare
frauds. In this paper, we attempt to give a review on frauds in healthcare
industry and the techniques for detecting such frauds. With an emphasis on the
techniques used, determining the significant sources and the features of the
healthcare data we proposed a machine learning model to tackle the issues
related to the health insurance claims. The univariate and bivariate analysis are
applied on the data to know the features pattern and then proper visualization
of data to know which feature affects the most and a machine learning model is
built on the pre-processed data.
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TABLE OF CONTENT
CHAPTER NO.
TITLE PAGE NO.
1
INTRODUCTION 9
2
LITERATURE SURVEY
12
2.1 PROPOSED SYSTEM 15
2.2
EXISTING SYSTEM 15
3
SCOPE OF THE PROJECT
17
3.1
OBJECTIVES
17
3.2
PROJECT GOALS
17
3.3
FEASIBILITY STUDY
18
4
METHODOLOGY
19
4.1
LIST OF MODULES
19
4.2
ALGORITHM EXPLANATION 19
4.3
SYSTEM ARCHITECTURE 22
4.3.1
WORK FLOW DIAGRAM
23
5 RESULT
24
5.1 DATA VALIDATION
24
5.2 ACCURACY
29
6 CONCLUSION
31
6.1 FUTURE WORK
31
7
6.2 REFERENCE
31
6.3 APENDICES
32
6.3.1 CODING
32
6.3.2 SCREENSHOTS
71
6.3.3 PLAGARISM REPORT
73
