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            "value": "IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT",
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    "dates": {
        "published": "2022-05-11",
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        "pages": {
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    "abstract": {
        "value": "Patients depend on health insurance provided by the governmentsystems, private systems, or both to utilizethe high-priced healthcare expenses. This dependency on health insurance draws some healthcare service providers to commit insurance frauds. In this paper, we perform a comparative analysis on various classification algorithms, namely Support Vector Machine (SVM), Decision-Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), to detect the health insurance fraud. The effectiveness of the algorithms are observed on the basis of performance metrics: Precision, Recall and F1-Score",
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            "key": "ref1",
            "doi": "10.1109/icbk.2017.47",
            "unstructured": "W. Zhang and X. He, ―An anomaly detection method for Medicare fraud detection,‖ in Big Knowledge (ICBK), 2017 IEEE International Conference on. IEEE, 2017, pp. 309–314"
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            "doi": "10.1109/spices52834.2022.9774071",
            "unstructured": "A. Urunkar, A. Khot, R. Bhat and N. Mudegol, \"Fraud Detection and Analysis for Insurance Claim using Machine Learning,\" 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), THIRUVANANTHAPURAM, India, 2022, pp. 406-411, doi: 10.1109/SPICES52834.2022.9774071"
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            "doi": "10.1109/icmla.2016.0063",
            "unstructured": "R. A. Bauder and T. M. Khoshgoftaar, ―A probabilistic programming approach for outlier detection in healthcare claims,‖ in Machine Learning and Applications (ICMLA), 2016 15th IEEE International Conference on. IEEE, 2016, pp. 347–354"
        }
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