Into the bowels of depression: Unravelling medical symptoms associated with depression by applying machine-learning techniques to a community based population sample

Abstract

Background: Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machinelearning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study. Methods: A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and..