AI Based Depression Detection System

AI Based Depression Detection System

Mrs. Sumedha Ayachit 1, Shashank Ashturkar 2, Priya Kunwar 3, Nikita Landge 4, Rushikesh Sawant 5
1IT, JSCOE
2IT, JSCOE

3IT, JSCOE
4IT, JSCOE

5IT, JSCOE
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Abstract - Depression is a common mental, physical, and emotional health issue that has a big influence on people's lives and society. In a naturally occurring psychological balance, tension might be considered as a broad disruption. A general description of depression is a disruption of psychological homeostasis. Effective treatment and better patient outcomes depend on early detection and action. This abstract describes a novel artificial intelligence (AI)-based depression detection system that makes use of AI algorithms and methodologies. In order to provide a flexible and dynamic platform, this study presents a comprehensive system that includes real-time picture identification, voice recognition. By facilitating smooth interaction through voice commands, effective picture processing, and expedited data collecting, the goal is to improve user experience. Use deep learning techniques such as convolutional neural networks (CNN) and recurrent neural networks (RNN) to efficiently process multiple types of data. The model is trained to identify patterns and relationships between different areas, allowing for a comprehensive assessment of the patient's condition.

Key Words: Pre-processing, Segmentation, Machine Learning, Feature Extraction, Classification CNN Algorithm.