AI IN MENTAL HEALTH : DETECTING EARLY SIGNS OF INSTABILITY

     INTRODUCTION



Mental health care traditionally relies on therapy, counseling, and medication, but challenges like accessibility, stigma, and a shortage of professionals hinder support. With rising concerns, innovative solutions are needed. 

AI is revolutionizing mental health by improving diagnosis, treatment, and support. It analyzes speech, text, and behavior to detect early signs of conditions like depression and anxiety. AI-powered chatbots and predictive models offer 24/7 support, making care more accessible while reducing stigma. 


 OBJECTIVE


The objective of our project "AI in Mental Health: Detecting Early Signs of Instability" is to leverage artificial intelligence to enhance mental health awareness and support by identifying early signs of mental health issues, providing personalized recommendations, and enabling users to track their mood over time. 

Our aim is to create a secure environment for seeking help, facilitate community interaction, and equip administrators with tools to monitor user activity, ultimately promoting healthier lives for individuals.





 LITERATURE REVIEW


Artificial intelligence (AI) is increasingly used in mental health care to recognize early signs of mental instability, allowing early intervention and improved patient outcomes. Various AI-based methods were examined, including natural language (NLP) processing for the analysis of text data from social media and medical records, algorithms for machine learning for detection of mental health anomalies and language analysis, and stress-related changes in sound and pitch were examined. Additionally, while facial recognition and behavioral analysis can help identify emotional information, portable devices monitor physiological indicators such as heart rate and sleep patterns to recognize stress and fear. Despite this possibility, mental health AI faces challenges such as data protection concerns, distortions in AI models, lack of clinical validation, and difficulties in integrating AI into healthcare systems. Future research should focus on improving data quality, improving AI transparency, ensuring ethical compliance, and developing personalized AI models for mental health. AI can revolutionize mental health detection, but overcoming these challenges is crucial for successful use of them.



 

 EXISTING SYSTEM


Existing methods for mental health support, such as Woebot, Wysa, and IBM Watson Health, primarily focus on specific aspects like therapy, speech analysis, or biometric tracking. 

While these solutions provide valuable tools for emotional support and early detection of mental health issues, they often operate in isolation and lack several key features.

What They Lack ?

Existing AI-based mental health solutions lack key features such as strong user engagement, personalized recommendations, and authentication for privacy. 
They also miss out on mood trend visualization, community support, and admin monitoring for safety. Furthermore, these solutions often don’t offer access to external resources like professional help or therapy services, limiting their ability to provide comprehensive, real-time support.


PROPOSED SYSTEM


Our proposed solution is a user-friendly tool that uses machine learning techniques to assess user's mental health based on factors like Growing Stress, Mood Swings, and Changes in Habits. 

It employs algorithms like Random Forest, XG Boost and Hybrid Models Like Voting Classifiers for accurate predictions and offers personalized recommendations. 

The system tracks user's mental health over time, features an admin dashboard for user management, and includes a chat function for support among users. Its aim is to detect mental health issues early and provide valuable resources for improvement.

                                                                   

 DESIGN                                                                         




                                                                        

RESULT























                                                                    

 CONCLUSION


This project provides a user-friendly mental health support platform designed for today's generation. It combines predictive analytics, mood tracking, community interaction, and access to resources to offer personalized care. 

Users receive tailored recommendations and visual insights to better manage their mental health, while an admin monitoring system ensures safety. By fostering engagement and connection, this platform helps individuals take control of their well-being in a supportive and interactive way.


                                                                      

 REFERENCES

  

   Calvo, R. A., et al. (2020) – Natural language processing in mental health applications.

   Fitzpatrick, K. K., et al. (2017) – Delivering cognitive behavioural therapy via conversational agents.

   Schuller, B., et al. (2018) – Emotion recognition in AI-driven mental health assessments.

   Nguyen, T., et al. (2021) – Reinforcement learning for personalized therapy.

   Torous, J., & Roberts, L. W. (2019) – Ethical concerns in AI-driven mental health care. 

   De Choudhury, M., et al. (2023) – Benefits and Harms of Large Language Models in Digital Mental       Health.

   Dehbozorgi, R., et al. (2025) – The application of artificial intelligence in the field of mental health: a     systematic review.


Location: Hanamkonda, Telangana, India

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