Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome

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dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorShateri, Ahmadreza
dc.contributor.authorNasiri, Hamid
dc.contributor.authorYagin, Burak
dc.contributor.authorColak, Cemil
dc.contributor.authorAlghannam, Abdullah F.
dc.date.accessioned2024-08-04T20:55:55Z
dc.date.available2024-08-04T20:55:55Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMyalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP. The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.en_US
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2024R309]en_US
dc.description.sponsorshipThis research project is funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R309) , Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.identifier.doi10.7717/peerj-cs.1857
dc.identifier.issn2376-5992
dc.identifier.pmid38660205en_US
dc.identifier.scopus2-s2.0-85190294563en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.1857
dc.identifier.urihttps://hdl.handle.net/11616/101932
dc.identifier.volume10en_US
dc.identifier.wosWOS:001189018300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPeerj Incen_US
dc.relation.ispartofPeerj Computer Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMyalgic encephalomyelitis/chronic fatigue syndromeen_US
dc.subjectExplainable artificial intelli- genceen_US
dc.subjectMachine learningen_US
dc.subjectPrognostic modelen_US
dc.titleDevelopment of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndromeen_US
dc.typeArticleen_US

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