Journals / Mugla Journal of Science and Technology / 2020 / Cilt: 6 - Sayı: 1

ESTIMATION OF EMOTIONAL AND SOCIAL LONELINESS IN ELDERS WITH THE DEVELOPED ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION MODELS

Pages
50–62
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

When the performance criteria were analyzed, it was found that ANN was the best predictor of SL and EL values.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In recent years, with the increase in the amount of data, the development of the technology required for the analysis of this data has made it easier for artificial intelligence to enter all areas.
📊
Key Findings & Data:Root Mean Squared Error (RMSE) and Correlation Coefficient (R) parameters were used to evaluate the predictive performance of the expert system.
💡
Conclusion & Impact:When the performance criteria were analyzed, it was found that ANN was the best predictor of SL and EL values.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:ANN · Artificial Neural Network · MLR

Abstract

In recent years, with the increase in the amount of data, the development of the technology required for the analysis of this data has made it easier for artificial intelligence to enter all areas. In this study, "Loneliness Scale for the Elderly" was used to measure loneliness level as a dependent variable, and the predictability of emotional and social loneliness parameters obtained was investigated with artificial intelligence and statistical techniques. For this reason, various scales were used to examine Emotional Loneliness (EL), and Social Loneliness (SL) and various input parameters were used in the scales. In this study, we designed an expert system which uses the Artificial Neural Network (ANN) - Machine Learning Algorithm and Multiple Linear Regression (MLR) statistical methods to estimate the SL and EL values by feeding with input values. Root Mean Squared Error (RMSE) and Correlation Coefficient (R) parameters were used to evaluate the predictive performance of the expert system. When the performance criteria were analyzed, it was found that ANN was the best predictor of SL and EL values. Social or emotional loneliness of individuals can be estimated by entering the questionnaire responses that are not included in the sample through the expert system developed in this study.

🔖

Cite This Article

UZUNHİSARCIKLI, E., AKGÜL, H., KAVUNCUOĞLU, E. (2020). ESTIMATION OF EMOTIONAL AND SOCIAL LONELINESS IN ELDERS WITH THE DEVELOPED ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION MODELS. Mugla Journal of Science and Technology, Cilt: 6 - Sayı: 1, 50–62. https://ajindex.com/articles/293931

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Aiken, R. L., (1995). Aging: An Introduction to Gerontology (1th Edition). USA: Sage Publications
  2. Akgül H. ve Yeşilyaprak B., (2015). “Yaşlılar İçin Yalnızlık Ölçeği’nin Türk Kültürüne Uyarlaması: Geçerlilik ve Güvenirlik Çalışması” Yaşlı sorunları Araştırma Dergisi, 8(1), 34-45.http://dergipark.ulakbim.gov.tr/yasad/article/view/5000119293/5000117646 (Access Date: 27.12.2014)
  3. Azadi, S., Sepaskhah, A.R., (2011). Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks. Theor. Appl. Climatol
  4. Azadi S., Karimi-Jashni A., (2016). Verifying the performance of the artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran. Waste Management, 48, 14–23
  5. Beal, C., (2006). “Lonelıness In Older women: A Revıew Of The Lıterature”. Issues in Mental Health Nursing, 27, 795–813. DOI: 10.1080/01612840600781196 http://www.tandfonline.com/doi/abs/1 0.1080/01612840600781196 (Access Date: 18.11.2014)
  6. Beale, M.H., Hagan, M.T., Demuth, M.H., (2010). Neural Network ToolboxTM 7 User´s Guide. MathWorks Inc: Natick, MA
  7. Cattan, M., White M., Bond J. and Learmouth A., (2005). “Preventing Social İsolation And Loneliness Among Older People: A Systematic Review Of Health Promotion İnterventions”. Ageing & Society, 25, 41-67. DOI: 10.1017/S0144686X04002594.http://journals.cambridge.org (Access Date: 27.12.2014)
  8. Danış, M. Z., (2004). Yaşlıların Evde Bakım Gereksinimleri Ve Evde Bakıma İlişkin Düşünceleri: Başarılı Yaşlanma Ve Yaşlı Bakım Modelleri. Ankara: Türkiye Güçsüzler ve Kimsesizlere Yardım Vakfı yayını
  9. De Jong Gierveld, J., (1998). “A Review Of Loneliness: Concept And Definitions, Determinants, And Consequences”. Review in Clinical Gerontology, 8, 73-80
  10. Dykstra, P. A., (2009). “Older Adult Loneliness: Myths and Realities”. European Journal of Ageing, 6, 91–100. DOI: 10.1007/s10433-009-0110-3. (Access Date: 25.11.2015)
  11. Eshbaugh, E., (2009). “The Role Of Friends İn Predicting Loneliness Among Older Women Living Alone”. Journal of Gerontological Nursing, 35(5),13–16. http://www.ncbi.nlm.nih.gov/pubmed/19476188 (Access Date: 11.10.2014)
  12. Flott, L.W., (2012). Using the scatter diagram tool to compare data, show relationship.Met. Finish. 110, 33–35
  13. Fukuoka, Y., Matsuki, H., Minamitani, H., Ishida, A., 1998. A modified backpropagation method to avoid false local mini