Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine

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dc.authoridSchuchardt, Jan Philipp/0000-0003-1724-6325
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorBadrasawi, Manal
dc.contributor.authorAbu Al-Halawa, Diala
dc.contributor.authorPolo, Stephanny Vicuna
dc.contributor.authorAbu Khader, Rami
dc.contributor.authorAl-Taweel, Haneen
dc.contributor.authorAbu Alwafa, Reem
dc.date.accessioned2024-05-19T14:42:53Z
dc.date.available2024-05-19T14:42:53Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractPurposeThis study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students.MethodsWe employed K-means clustering analysis algorithm and Decision Tree (DT) technique to identify the association between anemia and vitamin and mineral intakes. We normalized and balanced the data based on anemia weighted clusters for improving ML models' accuracy. In addition, t-tests and Analysis of Variance (ANOVA) were performed to identify significant differences between the clusters. We evaluated the models on a balanced dataset of 755 female participants from the Hebron district in Palestine.ResultsOur study found that 34.8% of the participants were anemic. The intake of various micronutrients (i.e., folate, Vit A, B5, B6, B12, C, E, Ca, Fe, and Mg) was below RDA/AI values, which indicated an overall unbalanced malnutrition in the present cohort. Anemia was significantly associated with intakes of energy, protein, fat, Vit B1, B5, B6, C, Mg, Cu and Zn. On the other hand, intakes of protein, Vit B2, B5, B6, C, E, choline, folate, phosphorus, Mn and Zn were significantly lower in anemic than in non-anemic subjects. DT classification models for vitamins and minerals (accuracy rate: 82.1%) identified an inverse association between intakes of Vit B2, B3, B5, B6, B12, E, folate, Zn, Mg, Fe and Mn and prevalence of anemia.ConclusionsBesides the nutrients commonly known to be linked to anemia-like folate, Vit B6, C, B12, or Fe-the cluster analyses in the present cohort of young female university students have also found choline, Vit E, B2, Zn, Mg, Mn, and phosphorus as additional nutrients that might relate to the development of anemia. Further research is needed to elucidate if the intake of these nutrients might influence the risk of anemia.en_US
dc.description.sponsorshipBundesministerium fr Bildung und Forschungen_US
dc.description.sponsorshipWe would like to thank the participants who contributed their time to this project.en_US
dc.identifier.doi10.1007/s00394-024-03360-8
dc.identifier.issn1436-6207
dc.identifier.issn1436-6215
dc.identifier.pmid38512358en_US
dc.identifier.scopus2-s2.0-85188445706en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s00394-024-03360-8
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5296
dc.identifier.wosWOS:001188401500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofEuropean Journal of Nutritionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectIron Deficiency Anemiaen_US
dc.subjectNutrient Intakeen_US
dc.subjectDietary Patternsen_US
dc.subjectClassification And Regression Treeen_US
dc.subjectMachine Learningen_US
dc.subjectK-Means Analysisen_US
dc.titleIdentification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestineen_US
dc.typeArticleen_US

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