Hanife AKGÜL, Esma UZUNHİSARLIKCI, Erhan KAVUNCUOĞLU

GELİŞMİŞ YAPAY SİNİR AĞLARI VE ÇOKLU DOĞRUSAL REGRESYON MODELLERİYLE YAŞLILARDA DUYGUSAL VE SOSYAL YALNIZLIK TAHMİNİ

Son yıllarda, veri miktarının artması ile birlikte bu verinin analizi için gerekli teknolojinin gelişmesi yapay zekanın her alana girişini kolaylaştırmıştır. Bu çalışmada, bağımlı değişken olan yalnızlık düzeyini ölçmek için "Yaşlılar İçin Yalnızlık Ölçeği" kullanılmış ve elde edilen duygusal ve sosyal yalnızlık parametrelerinin tahmin edilebilirliği yapay zeka ve istatistiksel teknikler araştırılmıştır. Bu nedenle, Duygusal Yalnızlık (EL) ve Sosyal Yalnızlık (SL)'ı incelemek için “YİYÖ” Yaşlılar İçin Yalnızlık Ölçeği’nden elde edilen veriler kullanılmıştır. Çalışmada, girdi değerleri ile beslenerek SL ve EL değerlerini tahmin eden, Yapay Sinir Ağları (ANN) – Makine Öğrenme Algoritması ve Çoklu Linee Regresyon (MLR) – İstatistiksel Yöntemlerini kullanan bir uzman sistem tasarlanmıştır. Sistemin, tahmin etme performansını değerlendirmek için Kök ortalama karesel hata (RMSE) ve Korelasyon Katsayısı (R) parametreleri kullanılmıştır. Performans kriterleri analiz edildiğinde SL ve EL değerlerini en iyi tahmin eden modelin ANN olduğu görülmüştür. Bu çalışmada geliştirilen uzman sistem aracılığı ile örneklem içerisinde bulunmayan anket cevaplarının uzman sisteme girişi yapılarak kişilerin sosyal veya duygusal yalnızlığı tahmin edilebilecektir.

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

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

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