Unraveling Depression Using Machine Intelligence

Artificial intelligence is a field at the intersection of computer science, mathematics, philosophy and neuroscience. Its core competency lies in shedding light on data intensive fields by generating complex relations amongst data points. Depression is a mental condition which has bemused psychologists due to its eccentric display in each individual. In this paper we peruse various sensory signals and suggest ways by which they can be exploited to effectively capture concealed patterns. Moreover we also review and suggest a number of learning models that can accurately classify and recursively pinpoint the intensity of depression. Citations to biologically inspired models are also made.