Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics

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dc.authoridArdigò, Luca Paolo/0000-0001-7677-5070
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridgormez, yasin/0000-0001-8276-2030
dc.authoridYagin, Burak/0000-0001-6687-979X
dc.authoridAlkhateeb, Abedalrhman/0000-0002-1751-7570
dc.authorwosidArdigò, Luca Paolo/H-8955-2019
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidgörmez, yasin/JEF-8096-2023
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorYasar, Seyma
dc.contributor.authorGormez, Yasin
dc.contributor.authorYagin, Burak
dc.contributor.authorPinar, Abdulvahap
dc.contributor.authorAlkhateeb, Abedalrhman
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2024-08-04T20:54:57Z
dc.date.available2024-08-04T20:54:57Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDiabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 +/- 1.88) % accuracy, (89.33 +/- 1.80) % precision, (91.24 +/- 1.67) % recall, (89.37 +/- 1.52) % F1-Score, and (97.00 +/- 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.en_US
dc.identifier.doi10.3390/metabo13121204
dc.identifier.issn2218-1989
dc.identifier.issue12en_US
dc.identifier.pmid38132885en_US
dc.identifier.scopus2-s2.0-85180693834en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/metabo13121204
dc.identifier.urihttps://hdl.handle.net/11616/101741
dc.identifier.volume13en_US
dc.identifier.wosWOS:001130571500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofMetabolitesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjecttype 2 diabetesen_US
dc.subjectdiabetic retinopathyen_US
dc.subjectexplainable artificial intelligenceen_US
dc.subjectbiomarkers discoveryen_US
dc.subjectdiagnosticen_US
dc.subjectBayesian optimizationen_US
dc.titleExplainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabeticsen_US
dc.typeArticleen_US

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