Analysis of the symptoms of depression — a neural network approach

Abstract

The purpose of this study is to determine the individual contribution, or importance number, of the symptoms to an analysis of depression, utilizing a neural network model. In addition, the presence of hopelessness and somatic complaints was examined, to determine their relevance to depression. Using Wave 1 data from Duke University’s contribution in the Epidemiological Catchment Area (ECA) study, we created a mathematical model, a neural network, to map the relationship of nine symptoms of major depression, hopelessness and somatic complaints to the presence or absence of the formal diagnosis of depression, and performed a contribution analysis. The contribution analysis using the neural network revealed that the symptoms with the greatest impact on the occurrence of depression, or with the largest importance number 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 concentration, in that order. Hopelessness and somatic symptoms were the lowest in their contribution to the diagnosis of depression. The study thus provides the hierarchy of the symptoms of depression and supports the DSM classification of major depression.