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@article{162527,
        author = {N. Nikitha Reddy and P. Mahidhar Reddy and K. Rithik Reddy and G. Kadirvelu},
        title = {Medicare fraud detection using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {372-377},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162527},
        abstract = {This project is dedicated to building big data solutions with tangible applications at the intersection of healthcare and insurance industry. This Capstone project will build a Medicare Fraud Detection model to analyze open data and predict/detect the fraudulent Medicare providers based on fraud patterns, anomaly analysis and geo-demographic metrics, with FDA drug data’s help this model is also trying to figure out the Opiate prescriptions and Overdoses related fraudulence. All datasets will be based solely on publicly available Medicare data from the Centers for Medicare and Medicaid Services (CMS), LEIE and other open data resources. },
        keywords = {Machine learning, Class imbalance,
Medicare fraud, Anomaly detetion, CatBoost, XGBoost, LightGBM, Gradient boosted machines, Sampling 
},
        month = {},
        }