{
  "abstract": "Clustering can organize heterogeneous healthcare data into clinically or operationally meaningful groups, but evidence of real decision-support use remains uneven. This systematic review examines healthcare domains, data types, clustering methods, validation practices, and implementation gaps in clustering-based Healthcare Decision Support Systems (DSS). Following PRISMA 2020, Scopus records published from 2015 to 2025 were screened using predefined eligibility criteria. Eighty-four records were identified; 36 remained after the open-access filter, 19 full-text reports were assessed, and 10 primary empirical studies were included. The studies covered fraud auditing, ICU profiling, chronic-disease risk and transition analysis, gestational-diabetes support, trauma pathways, radiology text, patient segmentation, and smart-home activity recognition. K-Means was the most frequently reported method, followed by Markov-based approaches, while other clustering techniques appeared less often. Most studies relied on retrospective, internal, or expert-based validation; only one reported prospective evaluation of a deployed web-based clinical DSS. Overall, clustering shows clear analytical value, but evidence for routine clinical deployment and patient-outcome improvement remains limited.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Francka Sakti Lee"
    },
    {
      "name": "Catur Edi Widodo"
    },
    {
      "name": "Johanes Fernandes Andry"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/412vwkk4pb6fv.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1801",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1801",
  "id": "412vwkk4pb6fv",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1801",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "3605|Artificial Intelligence in Healthcare"
    ]
  ],
  "openalex": "W7223363943",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1801/1087",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/412vwkk4pb6fv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:48Z",
      "native_id": "da20690cf54d4ab99862966fcafdb7e9",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1801"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA1-939",
      "subfield": "general"
    }
  ],
  "title": "Clustering-Based Data Mining for Healthcare Decision Support Systems: A Systematic Review",
  "title_text": "Clustering-Based Data Mining for Healthcare Decision Support Systems: A Systematic Review",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:48Z",
  "url": "https://research.draconitic.com/paper/412vwkk4pb6fv",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}