Responsive Website

Mental Health
Condition Prediction

for Leading Anxiety Awareness
Organization

AI in Mental Health
Behavioral Analysis
Social Media Analytics
Mental Health Prediction
 Anxiety Awareness
Machine Learning
Video Analysis
Natural Language Processing

Overview

A renowned non-profit organization dedicated to anxiety awareness required an advanced solution to predict mental health conditions by analyzing behaviors from videos and social media posts. Traditional methods of mental health assessment are often time-consuming and subjective. This research project aimed to leverage AI and machine learning technologies to provide a more objective, efficient, and scalable approach to mental health condition prediction.

Problems

1.Traditional mental health assessments are time-consuming and subjective, leading to potential inconsistencies.

2.There is a need for a scalable solution to analyze large volumes of data from videos and social media posts to predict mental health conditions.

3. The organization required a modern solution to streamline the mental health prediction process and improve early detection and intervention.

Key Requirements

1. Development of an AI-driven system to analyze behaviors from videos and social media posts.

2. Implementation of machine learning algorithms to predict mental health conditions based on behavioral data.

3. Integration of natural language processing (NLP) to analyze text from social media posts.

4. Real-time data processing and analysis for timely predictions.

5. Creation of a secure database to store and manage sensitive data.

6. Ensuring the highest level of data security and privacy protection for users' information.

Maintenance and Support: The admin interface provides tools for monitoring performance, updates, and technical support.

Responsive Design: The web app and admin interface are compatible across various devices and screen sizes.

Our Solutions

Behavioral Analysis from Videos: Implemented computer vision techniques using OpenCV to analyze facial expressions, body language, and other behavioral indicators from videos.
Social Media Text Analysis: Developed NLP models to analyze text from social media posts, identifying patterns and indicators of mental health conditions.
Machine Learning Predictions: Trained machine learning models using TensorFlow to predict mental health conditions based on analyzed behavioral and text data.
Real-time Data Processing: Ensured real-time data processing to provide timely predictions and interventions.
Data Management and Security: Created a secure database using MongoDB to store and manage sensitive user data, with robust encryption and privacy protection measures.
Integration and Scalability: Ensured the system could seamlessly integrate with existing platforms and scale to handle large volumes of data.

Design Process

This project involved teamwork between UI and UX designers to create an innovative solution. Our business and growth team stayed in constant communication, providing valuable insights throughout the process.

Results

  1. Improved Efficiency: The AI-driven system reduced the time required for mental health assessments by 50%, allowing for quicker interventions.

  2. Enhanced Accuracy: Achieved an 85% accuracy rate in predicting mental health conditions, providing more reliable assessments.

  3. Scalability:  The solution scaled to analyze large volumes of data, handling thousands of videos and social media posts daily.

  4. Data Security: Implemented advanced data security measures, ensuring the privacy and protection of users’ information.

  5. Early Detection and Intervention: The system enabled early detection of mental health conditions, facilitating timely support and intervention.

Harnessing AI to Predict Mental Health Conditions from Behaviors & Social Media

Current Status

The AI-driven mental health condition prediction system is fully operational, providing the Anxiety Awareness Organization with advanced tools to analyze behaviors and predict mental health conditions. The system has significantly improved the efficiency, accuracy, and scalability of mental health assessments, supporting the organization’s mission to raise awareness and provide timely interventions.

Tech Stack

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