An Explainable Artificial Intelligence Model Proposed for the Prediction of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and the Identification of Distinctive Metabolites

Basit Öğe Kaydı
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridMahmoud, Noha/0000-0003-0608-6661
dc.authoridAbdel Samee, Nagwan/0000-0001-5957-1383
dc.authoridRaza, Ali/0000-0001-5429-9835
dc.authoridYagin, Burak/0000-0001-6687-979X
dc.authoridAlkhateeb, Abedalrhman/0000-0002-1751-7570
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.authorwosidMahmoud, Noha/GPT-3706-2022
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorAlkhateeb, Abedalrhman
dc.contributor.authorRaza, Ali
dc.contributor.authorSamee, Nagwan Abdel
dc.contributor.authorMahmoud, Noha F.
dc.contributor.authorColak, Cemil
dc.contributor.authorYagin, Burak
dc.date.accessioned2024-08-04T20:54:56Z
dc.date.available2024-08-04T20:54:56Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating illness with a significant global prevalence, affecting over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. This study uses explainable artificial intelligence and machine learning techniques to identify discriminative metabolites for ME/CFS. Material and Methods: The model investigates a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics. Random forest methods together with other classifiers were applied to the data to classify individuals as ME/CFS patients and healthy individuals. The classification learning algorithms' performance in the validation step was evaluated using a variety of methods, including the traditional hold-out validation method, as well as the more modern cross-validation and bootstrap methods. Explainable artificial intelligence approaches were applied to clinically explain the optimum model's prediction decisions. Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis. The random forest model outperformed the other classifiers in ME/CFS prediction using the 1000-iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC. According to the obtained results, the bootstrap validation approach demonstrated the highest classification outcomes. Conclusion: The proposed model accurately classifies ME/CFS patients based on the selected biomarker candidate metabolites. It offers a clear interpretation of risk estimation for ME/CFS, aiding physicians in comprehending the significance of key metabolomic features within the model.en_US
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; [PNURSP2023R206]en_US
dc.description.sponsorshipThe authors express their gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R206), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.en_US
dc.identifier.doi10.3390/diagnostics13233495
dc.identifier.issn2075-4418
dc.identifier.issue23en_US
dc.identifier.pmid38066735en_US
dc.identifier.scopus2-s2.0-85179373726en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics13233495
dc.identifier.urihttps://hdl.handle.net/11616/101719
dc.identifier.volume13en_US
dc.identifier.wosWOS:001116032800001en_US
dc.identifier.wosqualityQ1en_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 artificial intelligenceen_US
dc.subjectmyalgic encephalomyelitis/chronic fatigue syndromeen_US
dc.subjectmetabolomics dataen_US
dc.subjectclinical classificationen_US
dc.titleAn Explainable Artificial Intelligence Model Proposed for the Prediction of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and the Identification of Distinctive Metabolitesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu