Prediction of effective sociodemographic variables in modeling health literacy: A machine learning approach

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dc.authoridİnceoğlu, Feyza/0000-0003-1453-0937
dc.authoridDeniz, Serdar/0000-0002-6941-4813
dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authorwosidİnceoğlu, Feyza/GVK-2847-2022
dc.authorwosidDeniz, Serdar/AAA-6094-2019
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.contributor.authorFeyza, Inceoglu
dc.contributor.authorSerdar, Deniz
dc.contributor.authorHilal, Yagin Fatma
dc.date.accessioned2024-08-04T20:54:37Z
dc.date.available2024-08-04T20:54:37Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIntroduction: Health literacy is becoming a more important concept for the effective use of health systems day by day. The main purpose of the study is to determine the importance levels of the variables by using Machine Learning methods in order to determine the main factors affecting health literacy, and to find the most important variables for health literacy. Material and methods: 1001 participants with a mean age of 18.05 +/- 0.81 standard deviations were included in the study. The European Health Literacy Scale was used to determine the health literacy level of the participants. The scale cut-off point is 25, and 516 (51.5%) of the participants have low health literacy and 485 (48.5%) have a high level of health literacy. In the study, XGBoost, random forest, logistic regression models from machine learning methods were used and indexes were calculated. Results: When the results of XGBoost, random forest, logistic regression models were evaluated, it was found that the model with the best performance was XGBoost. Sensitivity, specificity, F1-score, AUROC and Brier score values for the XGBoost models were obtained as 0.979, 0.965, 0.973, 0.983, 0.054 respectively. Conclusion: It was found that HL levels differed significantly in the variables of gender, age, class, family education, place of residence, economic situation, and covering health expenses (p < 0.05). According to the XGBoost model, it was found that the variable with the highest level of importance was reading the newspaper, while the variable with the lowest level of importance was the educational status of the mother. With the help of the established model, the basic variables that will affect the HL level were determined. The designed model will constitute the basic step of an supporting design system to improve physician-patient communication.en_US
dc.identifier.doi10.1016/j.ijmedinf.2023.105167
dc.identifier.issn1386-5056
dc.identifier.issn1872-8243
dc.identifier.pmid37572386en_US
dc.identifier.scopus2-s2.0-85167459980en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijmedinf.2023.105167
dc.identifier.urihttps://hdl.handle.net/11616/101510
dc.identifier.volume178en_US
dc.identifier.wosWOS:001062794100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofInternational Journal of Medical Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHealth literacyen_US
dc.subjectMachine learningen_US
dc.subjectXGBoosten_US
dc.subjectAUROCen_US
dc.subjectClassificationen_US
dc.titlePrediction of effective sociodemographic variables in modeling health literacy: A machine learning approachen_US
dc.typeArticleen_US

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