Analysis of the symptoms of depression - a neural network approach

The purpose of this study is to determine the individual contribution, or i mportance number, of the symptoms to an analysis of depression, utilizing a neural network model. In addition, the presence of hopelessness and somati c complaints was examined, to determine their relevance to depression. Usin g Wave 1 data from Duke University's contribution in the Epidemiological Ca tchment Area (ECA) study, we created a mathematical model, a neural network , to map the relationship of nine symptoms of major depression, hopelessnes s and somatic complaints to the presence or absence of the formal diagnosis of depression, and performed a contribution analysis. The contribution ana lysis using the neural network revealed that the symptoms with the greatest impact on the occurrence of depression, or with the largest importance num ber for depression, were sadness, loss of interest, tiredness and sleeping trouble, in that order. The most frequently reported symptoms, though, were sadness, sleeping trouble, suicidal ideation, tiredness and poor concentra tion, in that order. Hopelessness and somatic symptoms were the lowest in t heir contribution to the diagnosis of depression. The study thus provides t he hierarchy of the symptoms of depression and supports the DSM classificat ion of major depression. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved.