Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy

dc.authoridgormez, yasin/0000-0001-8276-2030
dc.authoridAlgarni, Abdulmohsen/0000-0002-7556-958X
dc.authoridArdigo, Luca Paolo/0000-0001-7677-5070
dc.authoridYAGIN, Fatma Hilal/0000-0002-9848-7958
dc.authoridCOLAK, Cemil/0000-0001-5406-098X
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorColak, Cemil
dc.contributor.authorAlgarni, Abdulmohsen
dc.contributor.authorGormez, Yasin
dc.contributor.authorGuldogan, Emek
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2024-10-26T18:09:30Z
dc.date.available2024-10-26T18:09:30Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractBackground: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). Methods: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro-Wilk test for normality, the Kruskal-Wallis H test for group differences, and the Mann-Whitney U test with Bonferroni correction for post-hoc comparisons. Results: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. Conclusion: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.
dc.description.sponsorshipDeanship of Scientific Re-search at King Khalid University [2/93/45]
dc.description.sponsorshipThis research was financially supported by the Deanship of Scientific Re-search at King Khalid University under research grant number (R.G.P.2/93/45). The numerical calculations reported in this paper were fully/partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources).
dc.identifier.doi10.3390/diagnostics14131364
dc.identifier.issn2075-4418
dc.identifier.issue13
dc.identifier.pmid39001254
dc.identifier.scopus2-s2.0-85198363533
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics14131364
dc.identifier.urihttps://hdl.handle.net/20.500.12418/30145
dc.identifier.volume14
dc.identifier.wosWOS:001269189800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectdiabetic retinopathy
dc.subjecttargeted metabolomics
dc.subjecthybrid explainable artificial intelligence
dc.subjectexplainable deep learning
dc.subjectbiomarkers
dc.titleHybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy
dc.typeArticle

Dosyalar