EARLY DETECTION OF MENTAL HEALTH ISSUES IN ADOLESCENTS

DEVELOP A PREDICTIVE MODEL TO IDENTIFY EARLY SIGNS OF MENTAL HEALTH ISSUES IN ADOLESCENTS USING SOCIAL MEDIA ACTIVITY, SCHOOL PERFORMANCE DATA, AND ANONYMOUS HEALTH RECORDS

Step 1: Social Media Activity:

• Users can link their social media accounts (e.g., Twitter, Instagram, Reddit, Whatsapp) or upload data such as posts, comments, or activity logs.
  • For demo purpose we are extracting user's REDDIT and WHATSAPP data.
  • whatsapp exported chats
• The app would analyze the emotional tone of their posts (Mentally Normal or Not Normal) by naive bayes,DENSE and LSTM ensemble modelling.
  • Mental issue prediction model from user's social media
• step 1 implementation

Step 2: School Performance Data:

• Users can upload academic reports or provide access to school performance data (e.g., grades, attendance records, remarks).
  • generating demo score cards
  • score cards
• The app will extract data from uploaded images into dataframes through tessaract OCR
• and then detect changes in performance that may correlate with mental health issues, such as SUDDEN DROPS IN GRADES, INCREASED ABSENTEEISM and sentiment in TEACHER REMARKS by Data Analaysis
• step 2 implementation

Step 3: Anonymous Health Records:

• Users can upload anonymized health records, including any previous psychological evaluations, physical health data, or history of mental health consultations.
• The app would analyze these records for any red flags related to mental well-being (e.g., patterns of anxiety, stress, or depression).

Step 4: AI Chatbot

• Description: The user interacts with an AI-powered chatbot that asks questions related to their daily life and mental state. Implementation:
• Conversational Analysis: The chatbot evaluates the user’s responses for sentiment and tone, detecting signs of potential mental health issues.
• Voice Assistance: Integration of voice recognition to assess the tone and emotion in spoken responses.
• Multilingual Support: The chatbot can communicate in multiple languages to make the service more accessible.

Goal: Provide real-time analysis of the user's mental state based on their responses and identify potential mental health issues.

Step 5: Personalized Recommendations and Resources

• Description: Based on collected data and analysis, provide users with personalized mental wellness tips, recommended readings, or mental health resources.
• Implementation:
  • Recommendation System: Generate personalized tips, such as relaxation techniques, mindfulness practices, or local mental health resources.
  • Integration with Mental Health Resources: Offer links to therapists, support groups, or crisis helplines.

Goal: Empower users to take proactive steps in mental health management with customized support.

Recommendations:

If a user shows signs of mental health issues, the application could recommend further evaluation or resources, such as speaking to a counselor, accessing mental health support services, or using self-help techniques.

Work flow:

Privacy & Ethical Considerations:

• Data Anonymization: Ensure that personal data is anonymized wherever possible, especially health records, to comply with data privacy laws.
• Consent: The application should have explicit user consent for accessing sensitive data like social media activity and health records.
• Transparency: Users should be informed about how their data will be used and analyzed, and they should have the option to delete their data anytime.