{
  "abstract": "In hypertension screening, previously diagnosed cases provide reliable positive labels, whereas individuals without a prior diagnosis comprise both undiagnosed hypertensive and non-hypertensive individuals, creating a canonical positive-unlabeled (PU) learning setting. We propose PU-Boost, a two-stage reliable-sample reconstruction framework that uses only readily obtainable demographic, behavioral, medical-history, and anthropometric variables. During training, only previously diagnosed hypertension cases are treated as labeled positives (P), while all remaining individuals are treated as unlabeled (U); no additional reference labels are supplied to the two-stage reconstruction procedure. Elastic Net first provides a global ranking of P versus U, after which a random forest refines the uncertain region and highly ambiguous observations are rejected. XGBoost is then trained on the reconstructed class-balanced pseudo-labeled sample. In the independent test set, PU-Boost achieved a sensitivity of 0.817 and a balanced accuracy of 0.682, the highest observed values among the compared models. It produced 729 false negatives, 41.1% fewer than standard XGBoost, although specificity decreased to 0.547. SHAP analysis indicated that established hypertension-related features, including age, body mass index, waist circumference, and family history, contributed strongly to model output. PU-Boost therefore provides a statistically motivated framework for reducing missed cases when screening for undiagnosed hypertension under incomplete diagnostic labeling.",
  "abstract_shown": true,
  "affiliations": [
    "University of Arizona"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "H. Wu"
    },
    {
      "name": "L. Shi"
    },
    {
      "name": "H. Yang"
    },
    {
      "name": "B. Li"
    },
    {
      "name": "Q. Wang"
    },
    {
      "name": "Z. Zhan"
    },
    {
      "name": "L. Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a5jsbq04tnwx0.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.04.26364708",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.04.26364708v1.full",
  "id": "a5jsbq04tnwx0",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.04.26364708v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1702|Machine Learning and Data Classification"
    ]
  ],
  "openalex": "W7220677989",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.04.26364708v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a5jsbq04tnwx0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:11Z",
      "native_id": "10.64898/2026.10.04.26364708",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.04.26364708v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "PU-Boost: ATwo-StageReliable-Sample Reconstruction Positive-Unlabeled Learning Framework for Screening Undiagnosed Hypertension",
  "title_text": "PU-Boost: ATwo-StageReliable-Sample Reconstruction Positive-Unlabeled Learning Framework for Screening Undiagnosed Hypertension",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:11Z",
  "url": "https://research.draconitic.com/paper/a5jsbq04tnwx0",
  "venue": "medRxiv",
  "versions": []
}