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Issue title: Special issue on Intelligent Biomedical Data Analysis and Processing
Guest editors: Deepak Gupta, Oscar Castillo and Ashish Khanna
Article type: Research Article
Authors: Kanksha, | Bhaskar, Aman | Pande, Sagar* | Malik, Rahul | Khamparia, Aditya
Affiliations: Department of Computer Science and Engineering, Lovely Professional University, Mumbai, India
Correspondence: [*] Corresponding author: Sagar Pande, Department of Computer Science and Engineering Lovely Professional University, Mumbai, India. E-mail: sagarpande30@gmail.com.
Abstract: 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.
Keywords: Isolated forest, fraud detection, machine learning, unsupervised learning
DOI: 10.3233/IDT-200052
Journal: Intelligent Decision Technologies, vol. 15, no. 1, pp. 127-139, 2021
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