Depression Detection Using Machine Learning

EasyChair Preprint 12731

Abstract

Millions of individuals all around the world suffer from depression, a significant mental health problem. The effectiveness of treating depression can be greatly enhanced by early identification and intervention. In this paper, we create a robust and effective model for depression identification by combining the capabilities of deep learning with conventional machine learning methods. We get information from a range of sources, including blogs, internet forums, and social networking sites. We use natural language processing methods to preprocess the input and extract pertinent information. The underlying patterns in the data are then discovered using a convolutional neural network (CNN) and a recurrent neural network (RNN). It is now possible to realise an automated system that can assist in identifying depression in individuals across a range of ages. Researchers have been searching for methods to accurately diagnose depression in order to detect it. Several investigations have been suggested in this context. In this work, we review a number of prior investigations that employed machine learning (ML) and artificial intelligence (AI) to identify depression. We integrate the output of these models with well-known machine learning techniques, such as decision trees and support vector machines (SVM), to increase the precision of depression diagnosis. Our findings demonstrate that in terms of accuracy, precision, and recall, the suggested hybrid model performs better than the methods currently in use. By the early and precise identification of depression, prompt intervention, and improved patient outcomes, this paradigm has the potential to completely transform the area of mental health

Keyphrases: Mental Health Assessment, Mood analysis Emotional recognition, Predictive Analytics, behavioral patterns, depression screening

@booklet{EasyChair:12731,
  author    = {Sai Kotesh Deekonda and Abhinash Atchala and Vishal Bommerla and Rajesh Kumar Raj Purohit Derani},
  title     = {Depression Detection Using Machine Learning},
  howpublished = {EasyChair Preprint 12731},
  year      = {EasyChair, 2024}}