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

Author

Listed:
  • 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)

  • Metin Toprak

    (Department of Economics, Istanbul Sabahattin Zaim University, Istanbul 34303, 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)

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.

Suggested Citation

  • Yuksel Bayraktar & Esme Isik & Ibrahim Isik & Ayfer Ozyilmaz & Metin Toprak & Fatma Kahraman Guloglu & Serdar Aydin, 2022. "Analyzing of Alzheimer’s Disease Based on Biomedical and Socio-Economic Approach Using Molecular Communication, Artificial Neural Network, and Random Forest Models," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7901-:d:850905
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    References listed on IDEAS

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    1. Hiroaki Kitano, 2002. "Computational systems biology," Nature, Nature, vol. 420(6912), pages 206-210, November.
    2. Andrew J. deMello, 2006. "Control and detection of chemical reactions in microfluidic systems," Nature, Nature, vol. 442(7101), pages 394-402, July.
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    Cited by:

    1. Guoliang Xu & Longchao Xu & Li Jia, 2022. "Research on Mortality Risk of Chinese Older Adults from the Perspective of Social Health," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
    2. Ayfer Ozyilmaz & Yuksel Bayraktar & Esme Isik & Metin Toprak & Mehmet Bilal Er & Furkan Besel & Serdar Aydin & Mehmet Firat Olgun & Sandra Collins, 2022. "The Relationship between Health Expenditures and Economic Growth in EU Countries: Empirical Evidence Using Panel Fourier Toda–Yamamoto Causality Test and Regression Models," IJERPH, MDPI, vol. 19(22), pages 1-17, November.

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