Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction

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dc.authoridYAGIN, Fatma Hilal/0000-0002-9848-7958
dc.authoridArdigo, Luca Paolo/0000-0001-7677-5070
dc.authoridAlgarni, Abdulmohsen/0000-0002-7556-958X
dc.authorwosidYAGIN, Fatma Hilal/ABI-8066-2020
dc.contributor.authorArslan, Ahmet Kadir
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorAlgarni, Abdulmohsen
dc.contributor.authorAL-Hashem, Fahaid
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2024-08-04T20:56:12Z
dc.date.available2024-08-04T20:56:12Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAcute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.en_US
dc.description.sponsorshipDeanship of Scientific Research at King Khalid University [R.G.P.2/93/45]en_US
dc.description.sponsorshipThis research was financially supported by the Deanship of Scientific Research at King Khalid University under research grant number (R.G.P.2/93/45).en_US
dc.identifier.doi10.3390/diagnostics14131353
dc.identifier.issn2075-4418
dc.identifier.issue13en_US
dc.identifier.pmid39001243en_US
dc.identifier.scopus2-s2.0-85198376583en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics14131353
dc.identifier.urihttps://hdl.handle.net/11616/102115
dc.identifier.volume14en_US
dc.identifier.wosWOS:001269238400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectExplainable Boosting Machineen_US
dc.subjectacute myocardial infarctionen_US
dc.subjectmetabolomicsen_US
dc.subjectbiomarkersen_US
dc.titleCombining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarctionen_US
dc.typeArticleen_US

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