Analyzing of Alzheimer’s Disease Based on Biomedical and Socio-Economic Approach Using Molecular Communication, Artificial Neural Network, and Random Forest Models

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dc.authorid0000-0002-6179-5746en_US
dc.contributor.authorBayraktar, Yüksel
dc.contributor.authorIşık, Esme
dc.contributor.authorIşık, İbrahim
dc.contributor.authorÖzyılmaz, Ayfer
dc.contributor.authorToprak, Metin
dc.contributor.authorKahraman Güloğlu, Fatma
dc.contributor.authorAydın, Serdar
dc.date.accessioned2022-07-18T10:29:34Z
dc.date.available2022-07-18T10:29:34Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Darende Meslek Yüksekokulu, Tıbbi Hizmetler ve Teknikler Bölümüen_US
dc.descriptionAcademic Editor: Mihajlo (Michael) Jakovljevicen_US
dc.descriptionReceived: 7 June 2022 / Revised: 24 June 2022 / Accepted: 26 June 2022 / Published: 28 June 2022en_US
dc.description(This article belongs to the Special Issue Global Health and Sustainable Development)en_US
dc.description.abstractAlzheimer’s disease will affect more people with increases in the elderly population, as the elderly population of countries everywhere generally rises significantly. However, other factors such as regional climates, environmental conditions and even eating and drinking habits may trigger Alzheimer’s disease or affect the life quality of individuals already suffering from this disease. Today, the subject of biomedical engineering is being studied intensively by many researchers considering that it has the potential to produce solutions to various diseases such as Alzheimer’s caused by problems in molecule or cell communication. In this study, firstly, a molecular communication model with the potential to be used in the treatment and/or diagnosis of Alzheimer’s disease was proposed, and its results were analyzed with an artificial neural network model. Secondly, the ratio of people suffering from Alzheimer’s disease to the total population, along with data of educational status, income inequality, poverty threshold, and the number of the poor in Turkey were subjected to detailed distribution analysis by using the random forest model statistically. As a result of the study, it was determined that a higher income level was causally associated with a lower risk of Alzheimer’s disease.en_US
dc.identifier.citationBayraktar, Y., Isik, E., Isik, I., Ozyilmaz, A., Toprak, M., Kahraman Guloglu, F., & Aydin, S. (2022). Analyzing of Alzheimer’s Disease Based on Biomedical and Socio-Economic Approach Using Molecular Communication, Artificial Neural Network, and Random Forest Models. Sustainability, 14(13), 7901.en_US
dc.identifier.doi10.3390/su14137901
dc.identifier.endpage15en_US
dc.identifier.issn2071-1050en_US
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-85133420339en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.3390/su14137901
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1152
dc.identifier.volume14en_US
dc.identifier.wosWOS:000822141300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorIşık, Esme
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlzheimer’s diseaseen_US
dc.subjectMolecular communicationen_US
dc.subjectAmyloid betaen_US
dc.subjectSocioeconomicen_US
dc.subjectRandom foresten_US
dc.subjectTurkeyen_US
dc.subjectNumber of received moleculesen_US
dc.subjectTotal populationen_US
dc.subjectİncome inequalityen_US
dc.titleAnalyzing of Alzheimer’s Disease Based on Biomedical and Socio-Economic Approach Using Molecular Communication, Artificial Neural Network, and Random Forest Modelsen_US
dc.typeArticleen_US

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