{
    "ok": true,
    "doi": "10.46243/jst.2023.v8.i06.pp45-57",
    "events": [
        {
            "seq": 1,
            "kind": "register",
            "at": "2026-09-29 22:00:23",
            "by": "Administrator (admin)",
            "by_kind": "user",
            "note": "registered at Crossref; record read from api.crossref.org",
            "changes": [
                {
                    "path": "abstract.lang",
                    "from": null,
                    "to": "en"
                },
                {
                    "path": "abstract.value",
                    "from": null,
                    "to": "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"
                },
                {
                    "path": "agents.0.name.family",
                    "from": null,
                    "to": "Mrs. K. Aarati "
                },
                {
                    "path": "agents.0.name.given",
                    "from": null,
                    "to": "Mrs. K. Aarati "
                },
                {
                    "path": "agents.0.role",
                    "from": null,
                    "to": "author"
                },
                {
                    "path": "agents.0.sequence",
                    "from": null,
                    "to": "first"
                },
                {
                    "path": "agents.1.name.org",
                    "from": null,
                    "to": "Longman Publishers"
                },
                {
                    "path": "agents.1.role",
                    "from": null,
                    "to": "publisher"
                },
                {
                    "path": "characters.0",
                    "from": null,
                    "to": "Language"
                },
                {
                    "path": "container.identifiers.0.medium",
                    "from": null,
                    "to": "electronic"
                },
                {
                    "path": "container.identifiers.0.type",
                    "from": null,
                    "to": "ISSN"
                },
                {
                    "path": "container.identifiers.0.value",
                    "from": null,
                    "to": "2456-5660"
                },
                {
                    "path": "container.issue",
                    "from": null,
                    "to": "9"
                },
                {
                    "path": "container.pages.first",
                    "from": null,
                    "to": "45"
                },
                {
                    "path": "container.pages.last",
                    "from": null,
                    "to": "57"
                },
                {
                    "path": "container.titles.0.type",
                    "from": null,
                    "to": "PrincipalTitle"
                },
                {
                    "path": "container.titles.0.value",
                    "from": null,
                    "to": "Journal of Science &amp; Technology"
                },
                {
                    "path": "container.type",
                    "from": null,
                    "to": "Journal"
                },
                {
                    "path": "container.volume",
                    "from": null,
                    "to": "7"
                },
                {
                    "path": "dates.date_type",
                    "from": null,
                    "to": "PublicationDate"
                },
                {
                    "path": "dates.online",
                    "from": null,
                    "to": "2022-05-11"
                },
                {
                    "path": "dates.published",
                    "from": null,
                    "to": "2022-05-11"
                },
                {
                    "path": "doi",
                    "from": null,
                    "to": "10.46243/jst.2023.v8.i06.pp45-57"
                },
                {
                    "path": "format",
                    "from": null,
                    "to": "smartscholars-doi-metadata/1.0"
                },
                {
                    "path": "identifiers.0.type",
                    "from": null,
                    "to": "DOI"
                },
                {
                    "path": "identifiers.0.value",
                    "from": null,
                    "to": "10.46243/jst.2023.v8.i06.pp45-57"
                },
                {
                    "path": "language",
                    "from": null,
                    "to": "en"
                },
                {
                    "path": "license.applies_to",
                    "from": null,
                    "to": "vor"
                },
                {
                    "path": "license.start",
                    "from": null,
                    "to": "2022-05-11"
                },
                {
                    "path": "license.url",
                    "from": null,
                    "to": "https://creativecommons.org/licenses/by/4.0/"
                },
                {
                    "path": "links.0.primary",
                    "from": null,
                    "to": true
                },
                {
                    "path": "links.0.return_type",
                    "from": null,
                    "to": "text/html"
                },
                {
                    "path": "links.0.url",
                    "from": null,
                    "to": "https://www.jst.org.in/index.php/pub/article/view/697"
                },
                {
                    "path": "links.1.purpose",
                    "from": null,
                    "to": "text-mining"
                },
                {
                    "path": "links.1.return_type",
                    "from": null,
                    "to": "application/pdf"
                },
                {
                    "path": "links.1.url",
                    "from": null,
                    "to": "https://www.jst.org.in/index.php/pub/article/download/697/628"
                },
                {
                    "path": "links.2.purpose",
                    "from": null,
                    "to": "text-mining"
                },
                {
                    "path": "links.2.return_type",
                    "from": null,
                    "to": "application/xml"
                },
                {
                    "path": "links.2.url",
                    "from": null,
                    "to": "https://www.jst.org.in/index.php/pub/article/download/697/1724"
                },
                {
                    "path": "links.3.purpose",
                    "from": null,
                    "to": "similarity-checking"
                },
                {
                    "path": "links.3.url",
                    "from": null,
                    "to": "https://jst.org.in/admin/uploads/Batch%207.pdf"
                },
                {
                    "path": "modes.0",
                    "from": null,
                    "to": "Visual"
                },
                {
                    "path": "record.issue_number",
                    "from": null,
                    "to": 1
                },
                {
                    "path": "record.registered",
                    "from": null,
                    "to": "2024-02-16"
                },
                {
                    "path": "record.registrant",
                    "from": null,
                    "to": "Longman Publishers"
                },
                {
                    "path": "record.source",
                    "from": null,
                    "to": "crossref-api"
                },
                {
                    "path": "record.source_agency",
                    "from": null,
                    "to": "Crossref (member 25296)"
                },
                {
                    "path": "record.updated",
                    "from": null,
                    "to": "2026-09-17"
                },
                {
                    "path": "references.0.doi",
                    "from": null,
                    "to": "10.1109/icbk.2017.47"
                },
                {
                    "path": "references.0.key",
                    "from": null,
                    "to": "ref1"
                },
                {
                    "path": "references.0.unstructured",
                    "from": null,
                    "to": "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"
                },
                {
                    "path": "references.1.doi",
                    "from": null,
                    "to": "10.1109/spices52834.2022.9774071"
                },
                {
                    "path": "references.1.key",
                    "from": null,
                    "to": "ref2"
                },
                {
                    "path": "references.1.unstructured",
                    "from": null,
                    "to": "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"
                },
                {
                    "path": "references.2.doi",
                    "from": null,
                    "to": "10.1109/icmla.2016.0063"
                },
                {
                    "path": "references.2.key",
                    "from": null,
                    "to": "ref3"
                },
                {
                    "path": "references.2.unstructured",
                    "from": null,
                    "to": "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"
                },
                {
                    "path": "referent",
                    "from": null,
                    "to": "Creation"
                },
                {
                    "path": "structural_type",
                    "from": null,
                    "to": "Digital"
                },
                {
                    "path": "titles.0.lang",
                    "from": null,
                    "to": "en"
                },
                {
                    "path": "titles.0.type",
                    "from": null,
                    "to": "PrincipalTitle"
                },
                {
                    "path": "titles.0.value",
                    "from": null,
                    "to": "IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT"
                },
                {
                    "path": "type",
                    "from": null,
                    "to": "JournalArticle"
                }
            ],
            "record_after": {
                "format": "smartscholars-doi-metadata/1.0",
                "doi": "10.46243/jst.2023.v8.i06.pp45-57",
                "referent": "Creation",
                "type": "JournalArticle",
                "structural_type": "Digital",
                "modes": [
                    "Visual"
                ],
                "characters": [
                    "Language"
                ],
                "titles": [
                    {
                        "value": "IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT",
                        "type": "PrincipalTitle",
                        "lang": "en"
                    }
                ],
                "identifiers": [
                    {
                        "type": "DOI",
                        "value": "10.46243/jst.2023.v8.i06.pp45-57"
                    }
                ],
                "agents": [
                    {
                        "role": "author",
                        "name": {
                            "given": "Mrs. K. Aarati ",
                            "family": "Mrs. K. Aarati "
                        },
                        "sequence": "first"
                    },
                    {
                        "role": "publisher",
                        "name": {
                            "org": "Longman Publishers"
                        }
                    }
                ],
                "dates": {
                    "published": "2022-05-11",
                    "date_type": "PublicationDate",
                    "online": "2022-05-11"
                },
                "language": "en",
                "container": {
                    "type": "Journal",
                    "titles": [
                        {
                            "value": "Journal of Science &amp; Technology",
                            "type": "PrincipalTitle"
                        }
                    ],
                    "identifiers": [
                        {
                            "type": "ISSN",
                            "value": "2456-5660",
                            "medium": "electronic"
                        }
                    ],
                    "volume": "7",
                    "issue": "9",
                    "pages": {
                        "first": "45",
                        "last": "57"
                    }
                },
                "links": [
                    {
                        "url": "https://www.jst.org.in/index.php/pub/article/view/697",
                        "return_type": "text/html",
                        "primary": true
                    },
                    {
                        "url": "https://www.jst.org.in/index.php/pub/article/download/697/628",
                        "purpose": "text-mining",
                        "return_type": "application/pdf"
                    },
                    {
                        "url": "https://www.jst.org.in/index.php/pub/article/download/697/1724",
                        "purpose": "text-mining",
                        "return_type": "application/xml"
                    },
                    {
                        "url": "https://jst.org.in/admin/uploads/Batch%207.pdf",
                        "purpose": "similarity-checking"
                    }
                ],
                "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",
                    "lang": "en"
                },
                "license": {
                    "url": "https://creativecommons.org/licenses/by/4.0/",
                    "start": "2022-05-11",
                    "applies_to": "vor"
                },
                "references": [
                    {
                        "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"
                    },
                    {
                        "key": "ref2",
                        "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"
                    },
                    {
                        "key": "ref3",
                        "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"
                    }
                ],
                "record": {
                    "registrant": "Longman Publishers",
                    "registered": "2024-02-16",
                    "updated": "2026-09-17",
                    "issue_number": 1,
                    "source": "crossref-api",
                    "source_agency": "Crossref (member 25296)"
                }
            },
            "url_after": "https://www.jst.org.in/index.php/pub/article/view/697",
            "sha256_before": null,
            "sha256_after": "c8dfb2618fabb89117672d7eb86e859c30805e7d014c4d7d3a2a4f61b2c73962"
        },
        {
            "seq": 2,
            "kind": "update",
            "at": "2026-09-29 23:59:41",
            "by": "Administrator (admin)",
            "by_kind": "user",
            "note": "record re-read from api.crossref.org",
            "changes": [
                {
                    "path": "agents.0.name.family",
                    "from": "Mrs. K. Aarati ",
                    "to": "Mrs. K. Aarati"
                },
                {
                    "path": "agents.0.name.given",
                    "from": "Mrs. K. Aarati ",
                    "to": "Mrs. K. Aarati"
                },
                {
                    "path": "container.titles.0.value",
                    "from": "Journal of Science &amp; Technology",
                    "to": "Journal of Science & Technology"
                }
            ],
            "record_after": {
                "format": "smartscholars-doi-metadata/1.0",
                "doi": "10.46243/jst.2023.v8.i06.pp45-57",
                "referent": "Creation",
                "type": "JournalArticle",
                "structural_type": "Digital",
                "modes": [
                    "Visual"
                ],
                "characters": [
                    "Language"
                ],
                "titles": [
                    {
                        "value": "IDENTIFYING HEALTH INSURANCE CLAIM FRAUDS USING MACHINE LEARNING CONCEPT",
                        "type": "PrincipalTitle",
                        "lang": "en"
                    }
                ],
                "identifiers": [
                    {
                        "type": "DOI",
                        "value": "10.46243/jst.2023.v8.i06.pp45-57"
                    }
                ],
                "agents": [
                    {
                        "role": "author",
                        "name": {
                            "given": "Mrs. K. Aarati",
                            "family": "Mrs. K. Aarati"
                        },
                        "sequence": "first"
                    },
                    {
                        "role": "publisher",
                        "name": {
                            "org": "Longman Publishers"
                        }
                    }
                ],
                "dates": {
                    "published": "2022-05-11",
                    "date_type": "PublicationDate",
                    "online": "2022-05-11"
                },
                "language": "en",
                "container": {
                    "type": "Journal",
                    "titles": [
                        {
                            "value": "Journal of Science & Technology",
                            "type": "PrincipalTitle"
                        }
                    ],
                    "identifiers": [
                        {
                            "type": "ISSN",
                            "value": "2456-5660",
                            "medium": "electronic"
                        }
                    ],
                    "volume": "7",
                    "issue": "9",
                    "pages": {
                        "first": "45",
                        "last": "57"
                    }
                },
                "links": [
                    {
                        "url": "https://www.jst.org.in/index.php/pub/article/view/697",
                        "return_type": "text/html",
                        "primary": true
                    },
                    {
                        "url": "https://www.jst.org.in/index.php/pub/article/download/697/628",
                        "purpose": "text-mining",
                        "return_type": "application/pdf"
                    },
                    {
                        "url": "https://www.jst.org.in/index.php/pub/article/download/697/1724",
                        "purpose": "text-mining",
                        "return_type": "application/xml"
                    },
                    {
                        "url": "https://jst.org.in/admin/uploads/Batch%207.pdf",
                        "purpose": "similarity-checking"
                    }
                ],
                "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",
                    "lang": "en"
                },
                "license": {
                    "url": "https://creativecommons.org/licenses/by/4.0/",
                    "start": "2022-05-11",
                    "applies_to": "vor"
                },
                "references": [
                    {
                        "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"
                    },
                    {
                        "key": "ref2",
                        "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"
                    },
                    {
                        "key": "ref3",
                        "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"
                    }
                ],
                "record": {
                    "registrant": "Longman Publishers",
                    "registered": "2024-02-16",
                    "updated": "2026-09-17",
                    "issue_number": 1,
                    "source": "crossref-api",
                    "source_agency": "Crossref (member 25296)"
                }
            },
            "url_after": "https://www.jst.org.in/index.php/pub/article/view/697",
            "sha256_before": "c8dfb2618fabb89117672d7eb86e859c30805e7d014c4d7d3a2a4f61b2c73962",
            "sha256_after": "7887f8355a5352c0c24cb3bcd7ae3300e65803fae12a6eb8c5919c954f004680"
        }
    ]
}