{
  "abstract": "Gallstone disease is a prevalent gastrointestinal condition with a substantial healthcare burden. While abdominal ultrasonography is the established first-line diagnostic modality, opportunistic risk stratification using routinely collected health parameters remains underexplored. This study evaluated the discriminatory capacity of four gradient boosting machine learning algorithms (XGBoost, LightGBM, CatBoost, and HistGBM) for classifying gallstone status using demographic, bioimpedance, and routine laboratory features. A retrospective clinical dataset of 319 patients (161 gallstone-positive individuals and 158 healthy controls) from Ankara VM Medical Park Hospital was analyzed. Model performance was assessed using repeated stratified 10-fold cross-validation, evaluated via AUC-ROC, accuracy, F1-score, sensitivity, specificity, Brier score, and decision curve analysis (DCA), with SHAP analyses employed for model interpretability. CatBoost achieved the highest discriminatory performance (AUC = 0.862±0.061, accuracy = 0.794±0.075) and the lowest Brier score (0.149±0.037), demonstrating statistically significant superiority over competing architectures (Friedman test, p < 0.001). Feature ablation revealed that bioimpedance parameters alone provided substantial independent discriminative signal (AUC = 0.797±0.079), approaching that of laboratory markers (AUC = 0.829±0.070), whereas demographic attributes alone were insufficient (AUC = 0.476±0.097). SHAP analyses identified C-reactive protein, Vitamin D, and bone mass as the most influential variables, alongside non-linear risk associations for visceral fat area and body mass index. Decision curve analysis demonstrated positive net clinical benefit across threshold probabilities of 0.10–0.80. These findings demonstrate that gradient boosting models, particularly CatBoost, can provide robust, interpretable retrospective classification of gallstone disease, offering proof-of-concept potential for opportunistic risk stratification in metabolic care settings and pre-imaging triage for ambiguous outpatient presentations rather than replacing first-line ultrasonography.",
  "abstract_shown": true,
  "affiliations": [
    "School of Data and Sciences, BRAC University, Dhaka, Bangladesh.",
    "Department of R&D, Chowa Giken Corporation, Sapporo, Japan.",
    "Department of Computer Science and Engineering, East Delta University, Chattogram, Bangladesh."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Kazi Sakib Hasan",
      "orcid": "0009-0003-3646-1204"
    },
    {
      "affs": [
        2
      ],
      "name": "Sumaiya Hossain Puspita"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/288rh82entfex.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0361572",
  "fulltext_url": "https://europepmc.org/articles/PMC13722331",
  "id": "288rh82entfex",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0361572",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Adipose Tissue"
    ],
    [
      "heading",
      "Algorithms"
    ],
    [
      "heading",
      "Anatomy"
    ],
    [
      "heading",
      "Applied Mathematics"
    ],
    [
      "heading",
      "Artificial Intelligence"
    ],
    [
      "heading",
      "Biliary System"
    ],
    [
      "heading",
      "Biological Tissue"
    ],
    [
      "heading",
      "Biology and Life Sciences"
    ],
    [
      "heading",
      "Boosting Algorithms"
    ],
    [
      "heading",
      "Computer and Information Sciences"
    ],
    [
      "heading",
      "Connective Tissue"
    ],
    [
      "heading",
      "Diagnostic Medicine"
    ],
    [
      "heading",
      "Diagnostic Radiology"
    ],
    [
      "heading",
      "Epidemiology"
    ],
    [
      "heading",
      "Gallbladder"
    ],
    [
      "heading",
      "Imaging Techniques"
    ],
    [
      "heading",
      "Liver"
    ],
    [
      "heading",
      "Machine Learning"
    ],
    [
      "heading",
      "Machine Learning Algorithms"
    ],
    [
      "heading",
      "Mathematics"
    ],
    [
      "heading",
      "Medical Risk Factors"
    ],
    [
      "heading",
      "Medicine and Health Sciences"
    ],
    [
      "heading",
      "Physical Sciences"
    ],
    [
      "heading",
      "Radiology and Imaging"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "heading",
      "Research and Analysis Methods"
    ],
    [
      "heading",
      "Simulation and Modeling"
    ],
    [
      "heading",
      "Ultrasound Imaging"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Biological tissue/Connective tissue/Adipose tissue"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Liver/Biliary system/Gallbladder"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms/Boosting algorithms"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Biological tissue/Connective tissue/Adipose tissue"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Liver/Biliary system/Gallbladder"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Ultrasound imaging"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Ultrasound imaging"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Machine learning algorithms"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Machine learning algorithms/Boosting algorithms"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Ultrasound imaging"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms/Boosting algorithms"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://europepmc.org/articles/PMC13722331?pdf=render",
  "pmcid": "PMC13722331",
  "pmid": "42961984",
  "published": "2026-10-30",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/288rh82entfex.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:20:55Z",
      "native_id": "10.1371/journal.pone.0361572",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0361572"
    },
    {
      "first_seen": "2026-11-02T03:21:21Z",
      "native_id": "MED:42961984",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42961984"
    },
    {
      "first_seen": "2026-11-03T12:22:51Z",
      "native_id": "PMC13722331",
      "source": "pmc",
      "url": "https://doi.org/10.1371/journal.pone.0361572"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "heading:Biology and Life Sciences",
      "subfield": "general"
    },
    {
      "field": "mathematics",
      "origin": "heading:Mathematics",
      "subfield": "general"
    },
    {
      "field": "medicine",
      "origin": "heading:Epidemiology",
      "subfield": "epidemiology-public-health"
    },
    {
      "field": "medicine",
      "origin": "plos:diagnostic radiology",
      "subfield": "imaging"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Gradient boosting models for gallstone disease classification: A comparative study using demographic and clinical data",
  "title_text": "Gradient boosting models for gallstone disease classification: A comparative study using demographic and clinical data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:51Z",
  "url": "https://research.draconitic.com/paper/288rh82entfex",
  "venue": "PLOS One",
  "versions": []
}