Predicting Elderly Depression: An Artificial Neural Network Model
| Autor: | Elahe Allahyari |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050103 clinical psychology
05 social sciences Ethnic group Artificial neural network model Regression analysis 030227 psychiatry Dilemma 03 medical and health sciences Behavioral Neuroscience Psychiatry and Mental health 0302 clinical medicine Marital status 0501 psychology and cognitive sciences Residence Psychology Biological Psychiatry Depression (differential diagnoses) Demography Sigmoid transfer function |
| Zdroj: | Iranian Journal of Psychiatry and Behavioral Sciences. 13 |
| ISSN: | 1735-9287 1735-8639 |
| DOI: | 10.5812/ijpbs.98497 |
| Popis: | Background:: The growing elderly population will bring serious problems in society. Depression is one of the major disorders of old age that can be affected by various factors such as gender, age, education, and place of residence, among others. Objectives:: However, most of these variables are not fully controllable, and there is an interaction between them. Therefore, it is often difficult to find relationships between these variables using regression models that have restrictive assumptions. In this study, artificial neural network models (ANNs) overcome this dilemma. Methods:: We determine the effect of variables of age, marital status, number of family members, income, employment status, homebound status, gender, place of residence (city or village), the number of chronic non-communicable diseases, and ethnicity on depression in the elderlies. Data were analyzed using SPSS22 software for 1,477 people aged 60-92 years. Results:: The best ANN model had 33 neurons in the hidden layer and a sigmoid transfer function in both the hidden and output layers. The preferred ANN model had a minimum sensitivity of 60% to determine the level of depression in the elderly. Conclusions:: This model introduced ethnicity, the number of households, the number of chronic diseases, age, and income as the most effective variables in predicting depression. |
| Databáze: | OpenAIRE |
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