Ensemble Hybrid Learning Methods for Automated Depression Detection

Project Code :TCMAPY1170

Objective

The objective of the "Ensemble Hybrid Learning Methods for Automated Depression Detection" project is to explore and develop a combination of ensemble learning techniques and hybrid models for the automated detection of depression. By integrating multiple machine learning algorithms and hybridizing them with techniques like deep learning, the project aims to enhance the accuracy and robustness of depression detection systems. Leveraging diverse data sources such as text, audio, and physiological signals, the project seeks to create a comprehensive framework capable of capturing nuanced patterns indicative of depression symptoms.

Abstract

The modern lifestyle changes have led to a notable rise in depression cases over the past century, despite advancements in diagnosing mental illnesses. Many individuals still go undetected, highlighting a need for more efficient detection methods. Automated techniques offer promise in identifying depression or individuals at risk. Effective depression detection relies on accurately representing language features and analyzing their use. In this study, text classifiers were trained specifically for depression detection. The primary goal was to enhance detection accuracy by evaluating two approaches: hybrid and ensemble methods. The findings indicate that ensemble models surpass hybrid models in classification accuracy. The success of ensemble models underscores the importance of combining multiple features and conducting proper feature selection for improved depression detection performance.

Keywords: depression, modern lifestyle, detection methods, text classifiers, ensemble models, feature selection

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.