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

Yuksel Bayraktar, Esme Isik, Ibrahim Isik, Ayfer Ozyilmaz, Metin Toprak, Fatma Kahraman Guloglu and Serdar Aydin
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Yuksel Bayraktar: Department of Economics, Istanbul University, Istanbul 34452, Turkey
Esme Isik: Department of Optician, Malatya Turgut Ozal University, Malatya 44700, Turkey
Ibrahim Isik: Department of Electrical Electronics Engineering, Inonu University, Malatya 44280, Turkey
Ayfer Ozyilmaz: Department of Foreign Trade, Kocaeli University, Kocaeli 41650, Turkey
Fatma Kahraman Guloglu: Department of Social Work, Yalova University, Yalova 77100, Turkey
Serdar Aydin: School of Health Sciences, Southern Illinois University Carbondale, 1365 Douglas Drive, Carbondale, IL 62901, USA

Sustainability, 2022, vol. 14, issue 13, 1-15

Abstract: Alzheimer’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.

Keywords: Alzheimer’s disease; molecular communication; amyloid beta; socioeconomic; random forest; neural network; Turkey; number of received molecules; total population; income inequality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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