Healthcare fraud continues to be a major challenge, costing billions annually and straining resources meant for patient care. A recent study explores how artificial intelligence, particularly association rule mining and unsupervised learning, can enhance fraud detection in Medicare & Medicaid claims. By analyzing large datasets using machine learning techniques such as Isolation Forest, CBLOF, ECOD, and OCSVM, researchers identified suspicious billing patterns and anomalies with greater accuracy.

Read more: https://pmc.ncbi.nlm.nih.gov/articles/PMC11046758/#Sec20

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