HearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysis

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dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorGormez, Yasin
dc.contributor.authorAlgarni, Abdulmohsen
dc.contributor.authorAl-Hashem, Fahaid
dc.contributor.authorDutta, Ashit Kumar
dc.contributor.authorAghaei, Mohammadreza
dc.date.accessioned2026-06-19T06:39:35Z
dc.date.available2026-06-19T06:39:35Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractHematological biomarkers have emerged as powerful tools in diagnosing Acute Heart Failure (AHF). This study introduces a novel diagnostic framework that integrates Explainable Artificial Intelligence (XAI) with Morris Sensitivity Analysis (MSA) to enhance both the interpretability and performance of machine learning models in AHF detection. A dataset consisting of 425 AHF patients and 430 controls was analyzed using eight machine learning models, including XGBoost, Histogram-based Gradient Boosting (histGB), Explainable Boosting Machine (EBM), and Random Forest. Model performance was evaluated through metrics such as AUC, accuracy, precision, recall, and Brier score. Hyperparameters were optimized via Bayesian optimization. Feature importance was assessed using MSA to identify variables with the highest predictive influence. The histGB model achieved the highest performance with an AUC of 87.93%. Both MSA and EBM consistently identified PDW, RDW-CV, NEU, NEU/LY ratio, age, and WBC as top predictive features across multiple models. These hematological markers demonstrated strong potential for early diagnosis and risk stratification in AHF patients. This study presents a clinically relevant, interpretable, and cost-effective diagnostic strategy that combines XAI with MSA for AHF prediction. The framework enhances clinical trust and provides a pathway toward personalized treatment by identifying accessible hematological biomarkers. The integration of explainability into AI models improves their transparency and applicability in real-world clinical settings.
dc.description.sponsorshipAlbert-Ludwigs-Universitt Freiburg im Breisgau (1016)
dc.description.sponsorshipOpen Access funding enabled and organized by Projekt DEAL.
dc.identifier.doi10.1038/s41598-025-23668-7
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.orcid0000-0002-7556-958X
dc.identifier.pmid41238596
dc.identifier.scopus2-s2.0-105021798740
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1038/s41598-025-23668-7
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5693
dc.identifier.volume15
dc.identifier.wosWOS:001616558200033
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectAcute Heart Failure
dc.subjectExplainable Artificial Intelligence
dc.subjectBiomarkers
dc.subjectMorris Sensitivity Analysis
dc.subjectDiagnostic Modeling
dc.subjectModel Interpretability
dc.subjectBayesian Optimization
dc.titleHearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysis
dc.typeArticle

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