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Amrutha Sachin Nagvekar - I am a 2nd-year student pursuing a BTech(Hons.) degree in Computer Science at RV University. My academic journey has been focused on exploring various facets of computer science, with a special interest in data analysis and web development. I am skilled in Python and C and front-end web technologies. I am passionate about creating innovative web applications that address real-world challenges. Beyond academics, I have a passion for drawing and painting, which allows me to balance my technical pursuits with creative expression. In the future, I aspire to go into the field of data analysis or web development.

Poster Abstract

INTRODUCTION: Mental health is a vital aspect of human well-being since it has an impact on our daily lives, productivity at work, and relationships within our communities. Poor mental health impacts our overall well-being. Mental health issues affect millions globally annually, impacting countless lives over their lifespan. It is estimated that 1 in 3 women and 1 in 5 men will experience major depression at some point in their lives. In 2019, approximately 970 million people globally were living with a mental disorder, with anxiety and depression being the most prevalent. Conditions like schizophrenia and bipolar disorder, though less common, carry significant social and economic burdens. To tackle mental health challenges, we need to focus on spreading awareness and ensuring people can easily get the right treatments and support.

OBJECTIVE: This project's primary goal is to use HPCC Systems, an open-source big data analytics platform, to show the prevalence and patterns of various mental health issues globally from the year 1990 to 2019. The project aims to predict depression prevalence using other mental health disorder data such as schizophrenia, anxiety, bipolar disorder, and eating disorders. The aim is to provide a comprehensive understanding about the increasing prevalence of mental health disorders through visualizations. These visualizations will help global efforts to improve mental health outcomes by understanding trends and tracking changes over time. Mental health problems are increasing day by day, making it necessary to take steps to address this growing concern. By using advanced data analysis and visualization techniques, the project seeks to uncover these trends and support better ways to help with mental health issues.

METHODOLOGY: The first step involved collecting reliable mental health dataset which include data on various mental health conditions across different nations. Next, performing data preprocessing to ensure consistency and handle missing values. The project's visualization component makes use of the Visualization bundle from HPCC Systems to create comparative visualizations. By employing various graphical representations, such as line charts to show trends over time and bar charts to compare prevalence rates across regions, the project provides a comprehensive view of the global mental health landscape. For analyzing trends over time, linear regression model is employed to predict the prevalence of depression using data on other mental health conditions such as schizophrenia, anxiety, bipolar disorder, and eating disorders. These analyses help us understand how mental health conditions affect people and guide healthcare providers in making informed decisions, leading to better mental health outcomes worldwide.

RESULT AND DISCUSSION: The linear regression model implemented in ECL achieved R-squared value of 0.3346, indicating that 33.46% of the variance in depression prevalence is explained by the model. The current model is a strong starting point, but there is substantial scope for improvement. Ongoing work will focus on enhancing the model's predictive power. Continuous evaluation and iteration will help to develop a more accurate model for predicting depression prevalence. Therefore, the final results are still a work in progress and forthcoming.

Presentation

In this Video Recording, Amrutha provides a tour and explanation of her poster content.

Global Analysis of Mental Health Prevalence:

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