An AI‑powered mental health monitoring system for early detection and safe support of stress, anxiety, and depression. It integrates emotion analysis, crisis screening, PHQ‑2/GAD‑2 assessments, and a Mental Health Index, using fine‑tuned NLP and RAG‑based CBT guidance to deliver personalized, context‑aware support.

An AI-powered Gradio web app that predicts mental health risk levels based on lifestyle inputs like sleep, stress, screen time, and more. The app uses a trained machine learning model and provides downloadable logs and a visual report of user predictions.

Mental health issues (like anxiety, depression, burnout) are often underdiagnosed or detected too late, especially among youth, working professionals, or underserved communities. There's a growing need for scalable, accessible tools for early detection and intervention

Mental health is a crucial aspect of overall well-being, and its early detection and intervention play a pivotal role in preventing severe mental health issues. This system presents an innovative approach to mental health detection using machine learning (ML) techniques.

AI-driven Mental Health Monitoring System that uses machine learning models to analyze student data and identify mental health conditions. The system aims to provide early detection and support by monitoring behavioral patterns, enabling timely intervention and promoting well-being in academic environments.

An AI-powered system for early detection of depression and suicide risk using NLP and deep learning, integrated with a React-based dashboard for real-time mental health monitoring.

A machine learning-based system that detects depression and anxiety through voice analysis, leveraging audio spectrograms and additional features to predict emotional states. Designed for early detection and mental health support.

an AI-driven early risk detection system that: Aggregates non-obvious health signals, such as: Lab trends over time Lifestyle data (sleep, activity, nutrition) Stress & mental health indicators Family history patterns Detects silent or early stage diseases before clinical diagnosis Generates risk probability scores, not binary outcomes .

AI-based system for early mental health risk detection using textual data from multiple consent-based sources such as journals and social media, developed as part of a Microsoft Azure Internship project.

The system we completed in our electronics lab project utilizes sensors to monitor physiological and behavioral data. Through data analysis and machine learning, it identifies patterns associated with different mental health conditions. The goal is early detection for timely intervention and support, contributing to proactive mental health care.

Predict and visualize employee burnout risk using daily health and work metrics. This project includes a synthetic dataset, notebook code for feature engineering and machine learning, and visualizations that support early detection and personalized wellness insights. Built as part of the Mental Health Burnout Detection System.

AI system for early detection of mental health conditions based on interactions on social media. • Fine-tuned BERT and LSTM models for text classification, detecting signs of depression, anxiety, and stress in user posts.

SenticGuard is a BERT-driven NLP framework for sentiment and mental health classification. Featuring transformer-based contextual embeddings, PyTorch training pipelines, and precision metrics, it enables scalable, real-time analysis for early detection and intervention systems.

This project uses machine learning to identify anxiety levels from user data, like text, speech, or physiological signals. It helps early detection and intervention, promoting mental wellness. The system analyzes patterns and provides insights to users or healthcare professionals. It aims to make mental health monitoring accessible and easy.

Suicide Detection in Twitter Streams 🛡️ An NLP-based mini-project designed to identify suicidal intent in real-time Twitter data. Utilizing Python and Machine Learning (Scikit-learn/NLTK), the system processes live streams to detect linguistic patterns of distress, aiming to assist in early mental health intervention.

Brainwave Emotions is a BCI-based project that analyzes EEG signals to identify human emotional states. Using real-time brainwave data from a BCI kit, the system detects Anger, Fear, Sadness, Depression, and Happiness, focusing on mental health monitoring, early depression detection, and guidance to improve emotional well-being globally!!

Mental Health AI is a Django-based web platform that uses Machine Learning to assess students’ mental well-being. It analyzes survey data on lifestyle and behavior to predict risk levels. The system offers personal dashboards, AI insights, PDF reports, and an admin panel to monitor high-risk cases, developed for a research thesis.

Mental Health Early Detection System Using Social Media Behaviour

Expert System for early detection mental health using Rule-Based Reasoning method

A comprehensive, production-ready multimodal mental health detection system that combines text and audio analysis for early detection of depression, anxiety, and other mental health conditions.

An AI-powered system for early detection of mental health risks and suicidal intent using Natural Language Processing and Deep Learning

Mental Health Support System (MHSS) is a web-based platform that provides digital mental health support through self-assessment tools, support groups, helplines, educational resources, and AI-assisted guidance. It aims to improve accessibility, early detection, and continuous mental-health care using an integrated PHP–MySQL–Python system.

AuroraMind is a digital mental health and psychological support system for students, offering 24/7 stigma-free assistance, early mental health risk detection, and institution-linked counselor integration, tailored for higher education environments in India.

The AI-based system enables early detection of mental health risk using ethical privacy preserving machine learning , supporting timely awareness and guided human intervention.

A Machine Learning and DBMS-based system for early detection of depression among students and working professionals using NLP on Reddit mental health data, with structured data storage in SQL and predictive analytics for mental health risk assessment.

AI-powered Mental Health Care System that leverages machine learning and NLP to provide early detection of stress, anxiety, and depression. It offers mood tracking, personalized support, and resources, aiming to improve accessibility and awareness in mental health care.

MH-EWSS is an intelligent speech analysis system designed to provide early detection of mental health conditions through voice-based biomarkers. By combining audio signal processing (MFCC) with machine learning models, the system aims to enable scalable, non-invasive, and real-time mental health monitoring.

Early-warning mental health risk detection platform that identifies sustained behavioral stress trends before clinical symptoms appear. Built as a decision-support system (not diagnosis), aligned with SDG 3 – Good Health & Well-Being.

Page 05 conclusion 02 The AI-based system enables early detection of mental health risks using ethical, privacy- preserving machine learning, supporting timely awareness and guided human intervention.

Built a machine learning–based Depression Detection system using social media text data. Applied NLP preprocessing, TF-IDF feature extraction, and multiple ML classifiers to identify depressive content, supporting early mental health risk detection.