Depression prediction using machine learning algorithms

Abstract

Nowadays, depression is one of the most common negative emotions between people. The goal of our paper is to predict whether the individuals has suffered depression according to the provided 23 features. This study classifies the characteristics and studies their impact on depression by random forest, naive bayes, Support Vector Machine (SVM) and neural network. The authors test all those methods on the public dataset, which conclude that random forest and neural network achieve the same top-1 accuracy (which is 86.8%). Furthermore, the authors also conduct the feature correlation to exploit the most important factors. Results show that the most two influential features leading to depression is age and education level, which means the group of teenagers is the easiest members to have depression emotion. The study also analyses how can people sort the situation and decrease the possibility of suffering from depression.