AI – Powered Depression Prediction

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.

Publication Details

Journal
Iconic Research and Engineering Journals
Published
2026-05-01
DOI
https://doi.org/10.64388/irev9i10-1717090
Primary Topic
Mental Health via Writing
Type
article
Field-Weighted Citation Impact
0.00
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article

AI – Powered Depression Prediction

Iconic Research and Engineering Journals
Mental Health via Writing
article

AI – Powered Depression Prediction

article en

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.

Iconic Research and Engineering JournalsVol. 9(10)
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Mental Health via Writing
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