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AI – Powered Depression Prediction
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I10-1717090
Abstract
The proposed is a sophisticated web-based platform designed to leverage artificial intelligence for the early detection and monitoring of depressive disorders. It integrates multiple analytical modules, including linguistic sentiment analysis, facial expression recognition, and vocal biomarker tracking, along with a clinical referral portal for professional intervention. The system enables users to input symptoms, undergo digital assessments, receive risk probability scores, and upload video journals to ensure accuracy in mental health monitoring. Additionally, the clinical module allows practitioners to access patient insights with data visualizations and longitudinal reports directly through the website. Developed using Python, React, and TensorFlow, the platform emphasizes data privacy, real-time processing, and empathetic user interaction, offering a professional, reliable, and accessible solution for modern psychiatric Artificial Intelligence, Depression Detection, Sentiment Analysis, Mental Health, Machine Learning Portal, Predictive Modelling, User Experience, Python, React, TensorFlow.
How to cite this paper
@article{1717090,
author = {Sharmila P., Sarathi R, Siva Prasanth B, Theppan TK, Udaya Bharaathi K; Vishal Raj N D},
title = {AI – Powered Depression Prediction},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {3811-3821},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717090.pdf},
abstract = {The proposed is a sophisticated web-based platform designed to leverage artificial intelligence for the early detection and monitoring of depressive disorders. It integrates multiple analytical modules, including linguistic sentiment analysis, facial expression recognition, and vocal biomarker tracking, along with a clinical referral portal for professional intervention. The system enables users to input symptoms, undergo digital assessments, receive risk probability scores, and upload video journals to ensure accuracy in mental health monitoring. Additionally, the clinical module allows practitioners to access patient insights with data visualizations and longitudinal reports directly through the website. Developed using Python, React, and TensorFlow, the platform emphasizes data privacy, real-time processing, and empathetic user interaction, offering a professional, reliable, and accessible solution for modern psychiatric Artificial Intelligence, Depression Detection, Sentiment Analysis, Mental Health, Machine Learning Portal, Predictive Modelling, User Experience, Python, React, TensorFlow.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1717090}
}