EARLY DIAGNOSIS OF DEPRESSION USING DEEP LEARNING AND NLP

Abstract

Depression is a common mental health disorder characterized by persistent feelings of sadness, low mood, or a loss of interest or pleasure in activities. It goes beyond normal fluctuations in mood and can significantly impair daily functioning and quality of life. Timely detection and accurate diagnosis of depression are crucial for effective treatment and support. AI-based depression detection involves the use of deep learning algorithms and data analysis techniques to analyse various types of data, such as text, voice, physiological signals, and micro-movements to identify patterns and indicators of depression. By leveraging large datasets and sophisticated algorithms, AI can help healthcare professionals and researchers improve the accuracy and efficiency of depression detection. This paper also discusses the use of chatbot in enhancing mood by giving emotional support, behavioural activation and resource provision. NLP is a branch of AI that employs the aforementioned computational tools, but it focuses on how computers handle and analyse human language in the form of unstructured text, including language translation, semantic comprehension, and information extraction.