Cite this DOI

Mrs. K. Aarati, M. K. A. (2022). IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT. *Journal of Science & Technology*, *7*(9), 45–57. https://doi.org/10.46243/jst.2023.v8.i06.pp45-57

@article{mrskaarati2022identifying,
  author    = {Mrs. K. Aarati, Mrs. K. Aarati},
  title     = {{IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {may},
  volume    = {7},
  number    = {9},
  pages     = {45--57},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i06.pp45-57},
  url       = {https://doi.org/10.46243/jst.2023.v8.i06.pp45-57},
  language  = {en},
  abstract  = {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}
}

TY  - JOUR
TI  - IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT
AU  - Mrs. K. Aarati, Mrs. K. Aarati
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/05/11/
VL  - 7
IS  - 9
SP  - 45
EP  - 57
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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
DO  - 10.46243/jst.2023.v8.i06.pp45-57
UR  - https://doi.org/10.46243/jst.2023.v8.i06.pp45-57
ER  -

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    "URL": "https://doi.org/10.46243/jst.2023.v8.i06.pp45-57",
    "title": "IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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        "date-parts": [
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                2022,
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    "volume": "7",
    "issue": "9",
    "page": "45-57",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "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",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i06.pp45-57 gives all four in one JSON answer.