Predicting Elderly Depression: An Artificial Neural Network Model

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.