Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study

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dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridYilmaz, Rustem/0000-0003-0587-3356
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidYilmaz, Rustem/GZM-1576-2022
dc.contributor.authorYilmaz, Rustem
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorColak, Cemil
dc.contributor.authorToprak, Kenan
dc.contributor.authorSamee, Nagwan Abdel
dc.contributor.authorMahmoud, Noha F.
dc.contributor.authorAlshahrani, Amnah Ali
dc.date.accessioned2024-08-04T20:55:55Z
dc.date.available2024-08-04T20:55:55Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIntroduction Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF. Methods In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model's efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model's incorporated risk factors. Results White blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF. Conclusion The results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.en_US
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2024R732]en_US
dc.description.sponsorshipThe authors express their gratitude to Princess Nourah bint Abdulrahman University Researchers.r The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia (Project Number PNURSP2024R732).en_US
dc.identifier.doi10.3389/fmed.2024.1285067
dc.identifier.issn2296-858X
dc.identifier.pmid38633310en_US
dc.identifier.scopus2-s2.0-85190436679en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3389/fmed.2024.1285067
dc.identifier.urihttps://hdl.handle.net/11616/101935
dc.identifier.volume11en_US
dc.identifier.wosWOS:001203118800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherFrontiers Media Saen_US
dc.relation.ispartofFrontiers in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectacute heart failureen_US
dc.subjectXGBoosten_US
dc.subjectexplainable artificial intelligenceen_US
dc.subjectSHAPen_US
dc.subjecthematological parametersen_US
dc.titleAnalysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective studyen_US
dc.typeArticleen_US

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