Identifying Signs of Depression in Today's World Through the Application of Machine Learning Algorithms

Abstract

Depression is a serious mental disorder that may lead individuals to endure continual fluctuations in mood, in addition to sensations of melancholy. It is possible for people to have these symptoms as a result of it. It is also possible for people to isolate themselves from other people as a result of it is now well acknowledged that it is a sickness that poses a threat of mortality to people in every area of the world. At present moment, everyone, from young children to elderly adults, is feeling depression; nevertheless, the great majority of people are not even aware of how terrible their mental state actually is. This is despite the fact that everyone is now experiencing depression. Everyone has a responsibility to always be vigilant in keeping a careful watch on the state of their own mental health. As a direct result of this, we are going to make use of a technique for detection that is founded on the concept of machine learning. During the work of our inquiry, we made use of a wide variety of algorithms, some of which include the following: the Naive Bayes (NB), K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Means and Random Forest (RF) approaches. For us, the results supplied by the Support Vector Machine classifier were the most accurate, and the accuracy of the SVM classifier was 97.67%. The results that we got from using this classifier were the best we could get.