• K.VENKATA CHALAPATHI
• D.SRIHARSHAVARDHAN

The general state of health among Indians is still being enhanced by developments in the medical and technological fields. Programs like Medicare are required in light of this ongoing development to assist in managing the high expenses linked to high-quality healthcare. Sadly, some people commit fraud for nefarious purposes and personal gain, which hinders Medicare's capacity to adequately meet the healthcare needs of the elderly and other eligible individuals. In an effort to curtail fraud, the Centers for Medicare and Medicaid Services (CMS) made a number of Big Data datasets available for different Medicare program components. This review concludes that in order to reduce healthcare costs, improve the effectiveness of detecting healthcare fraud, and provide top-notch healthcare systems, innovative machine learning techniques and recently identified sources of healthcare data will make fascinating research topics in the near future. Leveraging advanced machine learning algorithms and confusion matrix analysis, the platform meticulously evaluates medical procedures such as Brain FDG PET/CT Scans, Hemoglobin Tests, T3 and T4 Thyroid Level Assessments, and Spinal Fusion Surgeries. This system not only calculates fraud probability with high precision but also empowers patients with actionable insights, ensuring that they are not subjected to unjust charges or unethical treatments.

"Medicare Fraud Detection using Confusion Matrix in Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.j545-j549, march-2025, Available :http://www.jetir.org/papers/JETIR2503968.pdf

"Medicare Fraud Detection using Confusion Matrix in Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppj545-j549, march-2025, Available at : http://www.jetir.org/papers/JETIR2503968.pdf