Survey Paper on Fraud Detection in Medicare Using Machine Learning
DOI:
https://doi.org/10.61841/30w4za11Keywords:
Fraud Detection, Medicare, Machine LearningAbstract
Healthcare plays a major role in the growing population. Medicare systems provide effective remedies for the affordable healthcare systems. Increasing processing power, availability of big data, and advancements in statistical modeling have been accelerated due to the adoption of machine learning. There are recent fraudulent activities detected in Medicare systems. It takes a lot of time for humans to read, collect, categorize, and analyze the data. These fraudulent activities in Medicare systems, which can be tremendously reduced by using machine-learning methods. The survey currently focuses on the algorithm that contributes better results to fraud detection. Our survey predicts the comparative analysis in the detection of fraudulent activities using the various algorithm techniques.
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[1] Jameer Basha A., Palanisamy V., Purusothaman T, Efficient multimodal biometric authentication using fast
fingerprint verification and enhanced iris features 2011, Journal of Computer Science, Vol. 7, 5, pp. no.:
698-706.
[2] Bhuvaneswari K., Rauf H.A, Edgelet based human detection and tracking by combined segmentation and
soft decision, 2009, International Conference on Control Automation, Communication and Energy
Conservation, INCACEC 2009.
[3] Kumar P.S., Gayathri R., Senthamarai C., Priyadharshini M., Fernando P.S.A., Srinath R., Kumar V.V., V.V.,Kinetics, mechanism, isotherm, and thermodynamic analysis of adsorption of cadmium ions by surface-modified Strychnos potatorum seeds, 2012, Korean Journal of Chemical Engineering, Vol. 29, No. 12, pp. 1752-1760.
[4] Amanpreet Singh ; Narina Thakur ; Aakanksha Sharma, A review of supervised machine learning
algorithms, IEEE Proceedings, 3rd International Conference on Computing for Sustainable Global
Development (INDIACom), Mar. 2016.
[5] Xia ; David Lo ; Xinyu Wang ; Xiaohu Yang ; Shanping Li ; Jianling Sun, A Comparative Study of
Supervised Learning Algorithms for Re-opened Bug Prediction, DOI: 10.1109/CSMR.2013.43, Mar 2013.
[6] Susmita Ray, A Quick Review of Machine Learning Algorithms, DOI: 10.1109/COMITCon.
2019.8862451, Oct 2019.
[7] Suresh K Shirgave, Chetan J Awati, Rashmi More, Sonam S Patil A Review On Credit Card Fraud
Detection Using Machine Learning, International Journal of Scientific & Technology Research 8 (10):
1217-1220 · October 2019.
[8] Fraud Detection: How Machine Learning Systems Help Reveal Scams in Fintech, Healthcare, and
[9] SP Maniraj, Aditya Saini, Shadab Ahmed, Swarna Deep Sarkar, Credit Card Fraud Detection using
Machine Learning and Data Science, Volume 08, Issue 09 (September 2019).
[10] Vaishnavi NathDornadula, S. S.Geetha, Credit Card Fraud Detection using Machine Learning Algorithms, https://doi.org/10.1016/j.procs.2020.01.057, ELSEVIER ScienceDirect, 2019.
[11] C. Sindhu and G. Vadivu. “Effects of Adjective Verb Adverb on Sentiment Classification Using Support Vector Machine for Green Communication,” Journal of green engineering, vol. 10, issue 1, pp. 91–102, 2020
[12] M. Gayathri, C. Malathy, and Siddharth Singh, “MARK42: The Secured Personal Assistant Using Biometric Traits Integrated with Green IOT,” Journal of Green Engineering, vol. 10, issue 1, pp. 255–267, 2020.
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