AI Based system to detect Mental Illness

Inspiration

Depression, an insidious global malady, knows no boundaries and casts a shadow over the lives of millions. Its profound impact on individuals' well-being and societal harmony necessitates innovative approaches that transcend traditional boundaries. In response to this imperative, we present a pioneering project poised to revolutionize the landscape of depression detection and intervention. As the tendrils of depression extend across diverse demographics, our project addresses this challenge through a multi-faceted framework. Our objective is twofold: to identify signs of depression at its nascent stages and to equip mental health professionals with unprecedented tools for proactive intervention. Grounded in cutting-edge technology and psychological insights, our endeavor seeks to transcend conventional methods and usher in a new era of early detection and personalized care

What it does

As the tendrils of depression extend across diverse demographics, our project addresses this challenge through a multi-faceted framework. Our objective is twofold: to identify signs of depression at its nascent stages and to equip mental health professionals with unprecedented tools for proactive intervention. Grounded in cutting-edge technology and psychological insights, our endeavor seeks to transcend conventional methods and usher in a new era of early detection and personalized care. Comprising a trilogy of modules, our project amalgamates the power of artificial intelligence, machine learning, and empathetic interfaces to cater to the unique needs of individuals who grapple with depression. Each module contributes a distinct dimension to the holistic approach, ensuring that no one is left behind in the pursuit of well-being. In this introduction, we embark on a journey into the heart of our project, unraveling the intricate tapestry of innovation that weaves together technology and empathy. The subsequent sections will delve into each module's design, methodology, and transformative potential, underlining our commitment to reshaping the way we understand and address depression. As we traverse this path, we hold a beacon of hope, illuminating a future where early detection and targeted support are more than aspirations—they are tangible realities that hold the power to change lives.

How we built it

The global prevalence of depression has led to a significant demand for novel approaches to identify and address this mental health condition. This calls for innovative solutions that can detect depression early on, minimizing its impact on individuals' lives. In response to this need, we propose a revolutionary method to detect depression in its early stages through a multi-module system.

Despite the growing awareness of mental health issues, accurately identifying depression remains a challenge. Traditional methods often rely on self-reporting and periodic sessions with therapists, which can be resource-intensive and hinder timely intervention. To address this challenge, our project aims to develop a comprehensive solution that leverages technology to detect depression in a more accessible and proactive manner.

Our groundbreaking approach involves three distinct modules, each catering to different user profiles and offering unique features to empower both users and mental health professionals. The first module capitalizes on social media analytics to assess the emotional well-being of individuals. By analyzing users' posts, this module assists therapists in identifying potential indicators of depression. This module incorporates advanced Machine Learning algorithms, including Naive Bayes, to enhance the accuracy of its assessments.

For individuals who are less expressive about their emotions on social platforms, the second module introduces a creative solution: the compassionate bot. This conversational companion initiates friendly interactions, enhancing the diagnostic process. The third module solidifies the diagnosis process when irregularities persist even after the first two modules. It employs non-invasive electroencephalogram (EEG) tests to measure brain activity and provide further insights into users' mental states. The data from these assessments are processed through a combination of sophisticated machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), Gradient Boosting, Naive Bayes, and logistic regression, to support prediction analysis.

In summary, the existing methods for detecting depression are often reactive and resource-intensive. Our proposed innovative methodology revolutionizes this approach by utilizing technology and psychological insights to not only detect the early onset of depression but also equip mental health professionals with comprehensive resources for personalized treatment. This initiative paves the way for proactive mental healthcare, transcending barriers to offer timely assistance and hope to individuals in need. Therefore, the problem at hand is to create an effective and accessible system for early depression detection, bridging the gap between traditional methods and contemporary technological advancements in mental health care.

MODULES:

Social Media Analytics for Emotional Well-being (Module 1):This module harnesses the power of social media analytics to assess individuals' emotional well-being. By analyzing users' posts and interactions on various social platforms, it identifies potential signs of depression. Advanced Machine Learning algorithms, including Naive Bayes, are employed to accurately analyze the content, allowing mental health professionals to discern early indicators of depression and intervene proactively.
Compassionate Bot for Cordial Conversations (Module 2): Designed for individuals who may be reticent about sharing their emotions on social media, this module introduces a compassionate bot. The bot engages users in friendly and empathetic conversations, creating a safe space for individuals to express their feelings. Through these interactions, the bot gauges users' emotional states, aiding in the early detection of depression and offering an alternative means of assessment.
Neurological Insights through EEG Assessment (Module 3): When irregularities persist after the first two modules, Module 3 employs non-invasive electroencephalogram (EEG) tests to measure users' brain activity. This assessment provides deeper insights into users' mental states, contributing to a comprehensive understanding of their emotional well-being. The collected EEG data is then processed using sophisticated machine learning algorithms such as Random Forest, SVM, Naive Bayes, Gradient Boosting, and logistic regression, strengthening the accuracy of depression detection and supporting personalized treatment strategies by mental health professionals.

Challenges we ran into

The major challenge we faced with the project was in decoding the emojis and the images what the user posted in their timeline . This was also solved using Machine learning modules and they were converted into plain text so that they could be analysed.

What's next for AI Based system to detect mental illness

The future scope of this project would be to include all the famous social medias under one application so that it could be easy for the therapist to monitor their patients easily. As some patients could be active in certain social media and not in all platform.