Analysis of Machine Learning Algorithms for Predicting Depression

Abstract

The increasing use of technology can cause a life- style of less physical work. Also, the constant pressure on an individual can create a risk of mental disorder. These vulnerabilities include peer pressure, heart attack, depression, and many other effects. In this paper different approaches to predict depression are studied in detail. The mechanisms include collecting dataset through questionnaires asked to the person, posts on social media, text used throughout verbal communication and expressions on face. Result is derived from extracted information. Here output expected is that the person needs attention or not. In this research work different algorithms and classifiers of machine learning such as Decision Trees, SVM, Naive Bayes Classifier, Logistic Regression and KNN Classifier are analyzed to identify state of mental health in a target group. Target groups used for this identification process are public like students of high school, college students and working professionals. The paper also demonstrates an example in which Twitter scrapping tool Twint is used detect whether given Twit is depressive or not.