{
  "abstract": "Diabetes is a chronic disease that significantly increases the risk of serious complications such as cardiovascular disorders and kidney failure. Early detection through predictive modeling can lead to timely interventions and significantly improve patient health outcomes. Several machine learning approaches have been proposed for predicting diabetes, but the main focus has been on improving prediction accuracy, while interpretability has received limited attention. To address this gap, we present a robust and explainable machine learning framework based on a stacked ensemble model that uses Random Forest, Support Vector Machine, and Gradient Boosting as base learners and Catboost as the meta-learner. The model was trained on the PIMA Indians Diabetes dataset using a preprocessing pipeline that included standard scaling, analysis of variance (ANOVA)- F-score-based feature selection, and class balancing with the synthetic minority oversampling technique (SMOTE). The proposed ensemble model outperformed the latest methods with an accuracy of 86%. We integrated explainable AI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanation (SHAP) to enhance transparency, which provide both local and global interpretability by identifying the most influential features contributing to each prediction, thus supporting more informed and trustworthy decision-making in healthcare applications.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, University of Management and Technology, Lahore (Sialkot), Pakistan",
    "School of Computer Sciences, Universiti Sains Malaysia, USM Penang, Malaysia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Saher Fatima Awan"
    },
    {
      "affs": [
        1
      ],
      "name": "Kainat Irfan"
    },
    {
      "name": "Umair Muneer Butt"
    },
    {
      "name": "Sukumar Letchmunan"
    },
    {
      "name": "Fadratul Hafinaz Hassan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b3qbh3656n4jj.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0352313",
  "fulltext_url": null,
  "id": "b3qbh3656n4jj",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0352313",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3605|Artificial Intelligence in Healthcare"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Hormones/Insulin"
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      "/Computer and information sciences/Software engineering/Preprocessing"
    ],
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      "/Engineering and technology/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Medicine and health sciences/Endocrinology/Diabetic endocrinology/Insulin"
    ],
    [
      "plos",
      "/Medicine and health sciences/Endocrinology/Endocrine disorders/Diabetes mellitus"
    ],
    [
      "plos",
      "/Medicine and health sciences/Endocrinology/Endocrine disorders/Diabetes mellitus/Gestational diabetes"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Metabolic disorders/Diabetes mellitus"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Metabolic disorders/Diabetes mellitus/Gestational diabetes"
    ],
    [
      "plos",
      "/Medicine and health sciences/Women's health/Maternal health/Pregnancy"
    ],
    [
      "plos",
      "/Medicine and health sciences/Women's health/Obstetrics and gynecology/Pregnancy"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Chemical compounds/Organic compounds/Carbohydrates/Monosaccharides/Glucose"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Organic chemistry/Organic compounds/Carbohydrates/Monosaccharides/Glucose"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ]
  ],
  "openalex": "W7213312579",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0352313&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/b3qbh3656n4jj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pone.0352313",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0352313"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:endocrinology",
      "subfield": "endocrinology-metabolism"
    }
  ],
  "title": "Explainable diabetes prediction using a stacked ensemble framework",
  "title_text": "Explainable diabetes prediction using a stacked ensemble framework",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/b3qbh3656n4jj",
  "venue": "PLOS One",
  "versions": []
}