{
  "abstract": "To develop and evaluate a novel machine learning (ML) framework tailored to a clinical diabetes dataset and to assess whether demographic stratification enhances model performance and interpretability for multiclass diabetes classification. A clinical dataset of 264 patients' records was used to classify individuals including non-diabetic (36.4%), prediabetic (15.2%), and diabetic (48.5%) individuals. Several supervised learning models were trained using 80:20 train-test split and optimized using RandomizedSearchCV and 10-fold cross validation. Model performance was evaluated using the metrics accuracy, precision, recall and the F1-score. Area under the receiver operating characteristic curve (AUC) was calculated for the best generalizing model. A structured ML framework was developed for this dataset, incorporating preprocessing, model optimization, age stratification analysis age (<35 vs ≥35 years) and gender. SHAP was developed for model interpretability. Ensemble methods demonstrated superior performance in comparison to linear or single-tree approaches, with Gradient Boosting showing the most stable generalization with a test accuracy of 0.981 and stable cross validation accuracy of 0.972. AUC-ROC analysis using Gradient Boosting yielded good discriminative ability across the three diabetes classes: 0.991 (non-diabetic), 0.986 (prediabetic) and 0.972 (diabetic). Stratified analysis showed improved reliability in individuals aged ≥35 years (accuracy = 0.94, F1-score = 0.92), while performance in younger individuals was unstable due to small sample size. SHAP analysis identified HbA1c, BMI, and age as dominant predictors. This study presents a ML framework integrating age stratified modelling with explainable ML frameworks to improve interpretability. The findings offer clinically relevant results that can support clinical decision-making systems, individualized risk assessment, and potential applications for targeted intervention in diabetes progression.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ, United States."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nosizo Lukhele"
    },
    {
      "affs": [
        0
      ],
      "name": "Fahad Mostafa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5ssn3775jewjg.bib",
  "cited_by": 0,
  "doi": "10.3389/fdgth.2026.1952669",
  "fulltext_url": "https://europepmc.org/articles/PMC13713680",
  "id": "5ssn3775jewjg",
  "issns": [
    "2673-253X"
  ],
  "landing_url": "https://europepmc.org/article/MED/42953022",
  "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",
      "Original Research"
    ],
    [
      "openalex",
      "3605|Artificial Intelligence in Healthcare"
    ]
  ],
  "openalex": "W7221833543",
  "pdf_url": "https://europepmc.org/articles/PMC13713680?pdf=render",
  "pmcid": "PMC13713680",
  "pmid": "42953022",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/5ssn3775jewjg.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:41Z",
      "native_id": "MED:42953022",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42953022"
    },
    {
      "first_seen": "2026-10-31T12:24:11Z",
      "native_id": "PMC13713680",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fdgth.2026.1952669"
    }
  ],
  "subjects": [],
  "title": "Development of an explainable machine learning framework for early diabetes detection, multiclass classification, and risk stratification across age groups.",
  "title_text": "Development of an explainable machine learning framework for early diabetes detection, multiclass classification, and risk stratification across age groups.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:18Z",
  "url": "https://research.draconitic.com/paper/5ssn3775jewjg",
  "venue": "Frontiers in digital health",
  "versions": []
}