{
  "abstract": "Neonatal mortality remains a leading contributor to under-five deaths, yet early, individualized risk prediction is hampered by the rarity of the outcome and by limited attention to calibration and interpretability in prior models. We developed and stress-tested machine learning (ML) models for neonatal mortality prediction in a large, population-based cohort, integrating probability calibration, explainable AI, and exploratory unsupervised phenotyping. The development cohort of 61,517 neonates (131 deaths; class imbalance 1:469) was used to train L2-regularized logistic regression (LR) and LightGBM models on 60 routinely available perinatal features. performance was assessed by discrimination (AUROC, AUPRC), calibration, and decision-curve analysis, while SHAP and four unsupervised methods (UMAP, K-means, Isolation Forest, Apriori) characterized the underlying risk structure. Locked models then underwent a one-time case-only external stress test on 604 deceased neonates from a separate center and later period. LR achieved an AUROC of 0.955 (LightGBM, 0.944); at the optimal threshold (τ*=0.004) it captured 87.8% of deaths at 97.3% specificity, and positive net clinical benefit. In the external stress test, LR flagged 69.5% of deaths. Exploratory unsupervised analyses, fitted without outcome labels and evaluated post hoc against mortality, described a single high-risk phenotype; a K-means cluster comprising 6.7% of neonates contained 89.3% of all deaths. In this large, heterogeneous cohort, well-regularized ML models provided calibrated, interpretable, and early neonatal mortality risk stratification. The case-only design of the external cohort permits assessment of sensitivity only; external discrimination and calibration remain to be established, and prospective evaluation is warranted before clinical deployment.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Shaghayegh Moradi Alamdarloo",
      "orcid": "0000-0002-4064-4906"
    },
    {
      "name": "Amirreza Kanafchian",
      "orcid": "0009-0000-3174-5214"
    },
    {
      "name": "Hamideh Barzegar",
      "orcid": "0000-0003-1114-5937"
    },
    {
      "name": "Homeira Vafaei Cisakht"
    },
    {
      "name": "Nasrin Asadi"
    },
    {
      "name": "Fatemeh Asghari",
      "orcid": "0009-0005-4260-2696"
    },
    {
      "name": "Alireza Montaseri",
      "orcid": "0000-0002-0767-4230"
    },
    {
      "name": "Reza Bahrami",
      "orcid": "0000-0002-8554-4562"
    },
    {
      "name": "Khadijeh Bazrafshan"
    },
    {
      "name": "Atefe Hashemi",
      "orcid": "0000-0003-4045-1693"
    },
    {
      "name": "Ali Haghighi",
      "orcid": "0009-0001-8967-4161"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b1ybs1xg40vhc.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-76979-8",
  "fulltext_url": null,
  "id": "b1ybs1xg40vhc",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-76979-8",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3600|Maternal and Neonatal Healthcare"
    ]
  ],
  "openalex": "W7223050906",
  "pdf_url": "https://www.nature.com/articles/s41598-026-76979-8_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/b1ybs1xg40vhc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "10.1038/s41598-026-76979-8",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-76979-8"
    }
  ],
  "subjects": [],
  "title": "Neonatal mortality prediction under extreme class imbalance: calibrated supervised models and exploratory phenotype analysis in a population-based Iranian registry",
  "title_text": "Neonatal mortality prediction under extreme class imbalance: calibrated supervised models and exploratory phenotype analysis in a population-based Iranian registry",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:31Z",
  "url": "https://research.draconitic.com/paper/b1ybs1xg40vhc",
  "venue": "Scientific Reports",
  "versions": []
}