Medicare Fraud Detection using Machine Learning

References

[1] Bauder, R. A. and Khoshgoftaar, T. M., Medicare fraud detection using machine learning methods, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 858–865, 2017.

[2] Johnson, J. M., & Khoshgoftaar, T. M., Medicare Fraud Detection Using Neural Networks. Journal of Big Data, 6, Article No. 63, 2019.

[3] Conghai Zhang, Xinyao Xiao and Chao Wu, Medical Fraud and Abuse Detection System Based on Machine Learning. Int. J. Environ. Res. Public Health 2020.

[4] S. Lavanya1, S. Manoj Kumar, P. Mohan Kumar. Machine Learning Based Approaches for Healthcare Fraud Detection: A Comparative Analysis. Annals of R.S.C.B., ISSN:1583-6258, Vol. 25, Issue 3, 2021.

[5] A. Jenita Mary, S. P. Angelin Claret. Analytical study on fraud detection in healthcare insurance claim data using machine learning classifiers, AIP Conf. Proc. 2516, 240006, 2022.

[6] Hole Prajakta Parshuram, Prof. S. G. Joshi. A Comprehensive Analysis of Provider Fraud Detection through Machine Learning. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), Volume 3, Issue 2, 2023.

[7] Lekkala, L. R., Importance of Machine Learning Models in Healthcare Fraud Detection. Voice of the Publisher, 9, 207-215, 2023.

[8] https://www.kaggle.com/datasets/rohitrox/healthcare-provider-fraud-detection-analysis/data.

[9] /01BCERSUbcepqs†‡ˆ‰Š’“”¦§¨µôçØÎµÎµÎµάµÎ¬ ”†xj†^RFFh‘Fh‘F6�OJQJh‘Fh^~ 6�OJQJh‘Fh¾$ù6�OJQJh‘Fh¾$ù6�H*OJQJh‘Fh‘F6�H*OJQJh‘Fh^~ 6�H*OJQJh7Ÿh^~ H*OJQJ*h^~ h‘FOJQJh‘FH*OJQJh^~ h‘FOJPJQJh^~ h‘FH*OJQJh‘FOJPJQJh‘Fh^~ CJOJQJaJh^~ h^~ CJ(OJQJh7ŸCJ(OJPJQJ01‰Ý= > ? § [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

[10] [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

@article{1705865,
      author = {Pranjal Chaudhari, Pratibha Koli, Harshada Mali, Sumit Pawar, Prof. Manisha Patil},
      title = {Medicare Fraud Detection using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {11},
      pages = {650-655},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705865.pdf},
      abstract = {Medicare fraud is a significant issue that poses a threat to the integrity of the medicare system, leading to substantial financial losses and potentially compromising patient care. In response to this challenge, the utilization of machine learning models has emerged as a promising approach for detecting and preventing fraudulent activities within Medicare. This research paper proposes a machine learning approach for detecting fraud among healthcare providers. The approach involves utilizing machine learning algorithms to analyze diverse datasets containing information on billing patterns, patient demographics, service types, and geographic locations. By training the model on labelled data indicating instances of fraud, it learns to identify patterns and anomalies indicative of fraudulent behavior. Key findings from this study include the successful development of a machine learning model capable of accurately detecting healthcare provider fraud. The model demonstrates high precision, recall, and accuracy rates when tested on both training and unseen data, indicating its robustness and effectiveness.},
      keywords = {Medicare fraud, Machine Learning, Fraud detection, Support Vector Machine, Logistic Regression, LightGBM, Na?ve Bayes.},
      month = {May},
  }