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A Comprehensive View for Providing the Decision on Medicare Data

  • P. Naga JyothiEmail author
  • D. Rajya Lakshmi
  • K. V. S. N. Rama Rao
Conference paper
  • 16 Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 169)

Abstract

Decision making toward Medicare in the running environment has been taken topmost priority. Processing and understanding of Medicare data are a tricky challenge. The recommended system design carries in various stages to work insight to the problem. In this paper, we use machine learning algorithms for accurate decision on Medicare data. Specifically, the paper analyzes heterogeneous-typed data with the use of hierarchical grouping (HG) mechanismin at the preprocessing phase. Application of metrics like multiple aggregate, grouping and re-indexing simplifies the process in the both the phases. While in the development phase, detection outlier with the perspective of claims provided by the provider given at prior (history). Prior cost acts as good indicator for the decision. Use of statistical-based approach the outlier amount is detected. Random forest (RF) algorithm generates RF trees, and they were able to generate accurate results to choose cost of surgery (disease) from the provided data. Our system is useful to evaluate with reasonably low costs and error free, as demonstrated in experimentation on real-world datasets which are publicly available.

Keywords

Hierarchical grouping Heterogeneous Multiple aggregate Statistical approach Random forest 

References

  1. 1.
    Travaille, P., Müller, R.M., Thornton, D., Van Hillegersberg, J.: Electronic fraud detection in the US medicaid healthcare program: lessons learned from other industries. In: AIS Electronic Library (AISeL), AMCIS. pp. 1–10 (2011)Google Scholar
  2. 2.
    Shannon, C.E.: Mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commons. Rev. 5(1), 3–55 (2001)Google Scholar
  3. 3.
    Zhao, J., Papapetrou, P., Asker, L., Boström, H.: Learning from heterogeneous temporal Data in electronic health records. J. Biomed. Inform. Elsevier pp. 105–119 (2017)Google Scholar
  4. 4.
    Cunha, J., Neto, P.S., Rabelo, R.L., Santana, A.M.: Investigating the effects of class imbalance in learning the claim authorization process in the Brazilian health care market. IEEE, pp. 3265–3271 (2017)Google Scholar
  5. 5.
    Bauder, R.A., Richter, A., Herlandand, M., Khoshgoftaar, T.M.: Predicting medical provider specialties to detect anomalous insurance claims (2016)Google Scholar
  6. 6.
    Bauder, R.A., Khoshgoftaar, T.M.: Medicare fraud detection using machine learning methods. In: IEEE-Conference. pp. 858–865 (2017)Google Scholar
  7. 7.
    Christy, A., Meera Gandhi, G., Vaithyasubramanian, S.: Cluster-based outlier detection algorithm for healthcare data. International Symposium on Big Data and Cloud Computing. (ISBCC’15). pp. 209–215 (2015)Google Scholar
  8. 8.
    Lu, Y.: Thesis on Concept hierarchy in data mining: Specification Generation and Implementation. Simon Fraser University, Dec (1997)Google Scholar
  9. 9.
    Kadam, A., Powar, S.G.: Hybrid approach to outlier detection in medical data set. Asian J. Comput. Sci. Technol. 6(2), 18–22 (2017)Google Scholar
  10. 10.
    Agarwalla, N., Oanda, D., Modi, M.K.: Deep learning using retrictred boltzmann machines. Int. Journal Comput. Sci. Inf. Security. 7(3), 1552–1556 (2016)Google Scholar
  11. 11.
    Alam, M.R., Bennamoun, M., Togneri, R., Sohel, F.: A joint deep Boltzmann machine (jDBM) model for person identification using mobile phone data. IEEE Trans. Multimedia 19(2), 317–326 (2017)CrossRefGoogle Scholar
  12. 12.
    Friedman, J., Wegman, E.J., Gantz, D.T., Miller, I.J (eds.): Fitting functions to noisy data in high dimensions. In the Proceedings of 20th Symposium Interface Amer. Statistical. Assoc., pp. 13–43 (1988)Google Scholar
  13. 13.
  14. 14.
    Centers for medicare and medicaid services: research, statistics, data, and systems. https://www.cms.gov/research-statistics-data-and-systems/research-statistics-data-and-systems.html (2017)
  15. 15.
    Wu, X.: Metrics, techniques, and tools of anomaly detection: a survey. ebook, pp. 1–12 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • P. Naga Jyothi
    • 1
    Email author
  • D. Rajya Lakshmi
    • 2
  • K. V. S. N. Rama Rao
    • 1
  1. 1.Department of CSEK L Educational FoundationGunturIndia
  2. 2.Department of Computer Science and EngineeringJNTUK UCEVVizianagaramIndia

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