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ics-Data-Scientists-Essential/dp/1491952 962 Centers for Medicare & Medicaid Services data . (2024). https://data.cms.gov/summary-statistics-o n-use-and-payments/program-integrity-m arket-saturation-by-type-of-service/marke t-saturation-utilization-state-county Centers for Medicare and Medicaid Services. (2021). Medicare fraud & abuse: Prevent, detect, report. https://www.cms.gov/Outreach-and-Educ ation/Medicare-Learning-Network-MLN/ MLNProducts/Downloads/Fraud-Abuse MLN4649244.pdf Chen, Z. X., Hohmann, L., Banjara, B., Zhao, Y., Diggs, K., & Westrick, S. C. (2020). Recommendations to protect patients and health care practices from Medicare and Medicaid fraud. Journal of the American Pharmacists Association, 60(6), e60–e65. https://doi.org/10.1016/j.japh.2020.05.01 1 Federal Bureau of Investigation. (n.d.). Health care fraud . https://www.fbi.gov/investigate/white-col lar-crime/health-care-fraud GridSearchCV . (n.d.). Scikit-learn. https://scikit-learn.org/stable/modules/gen erated/sklearn.model_selection.GridSearc hCV.html Johnson, J. M., & Khoshgoftaar, T. M. (2023). Data-Centric AI for Healthcare Fraud Detection. SN Computer Science/SN Computer Science , 4 (4). https://doi.org/10.1007/s42979-023-0180 9-x Johnson, J.M., & Khoshgoftaar, T.M. Medicare fraud detection using neural networks. JBigData 6 , 63 (2019).

are practical and interactive, it is crucial to collaborate with policymakers and healthcare providers. References AdaBoostClassifier . (n.d.). Scikit-learn. https://scikit-learn.org/stable/modules/gen erated/sklearn.ensemble.AdaBoostClassif ier.html American Medical Association. (2021). Medicare Fraud & Abuse: Prevent, Detect, Report. In MLN Booklet . https://www.cms.gov/Outreach-and-Educ ation/Medicare-Learning-Network-MLN/ MLNProducts/Downloads/Fraud-Abuse MLN4649244.pdf Amponsah, A. A., Adekoya, A. F., & Weyori, B. A. (2022). A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology. Decision Analytics Journal , 1 (2), 100122. https://doi.org/10.1016/j.dajour.2022.100 122 BaggingClassifier . (n.d.). Scikit-learn. https://scikit-learn.org/stable/modules/gen erated/sklearn.ensemble.BaggingClassifie r.html Bauder, R., Da Rosa, R., & Khoshgoftaar, T. (2018). Identifying Medicare provider fraud with unsupervised machine learning. IEEE International Conference on Information Reuse and Integration (IRI) , 285–292. https://doi.org/10.1109/iri.2018.0005 Bruce, P., & Bruce, A. (2017). Practical Statistics for Data Scientists: 50 Essential Concepts. https://www.amazon.com/Practical-Statist

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