Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
Event Website
https://iotas.kennesaw.edu/
Document Type
Event
Start Date
15-4-2025 4:00 PM
Description
Mental health is an essential part of living a balanced and fulfilling life, but it is often overlooked compared to physical health. While physical health is important for performing daily activities, mental health plays a crucial role in how we manage stress, build connections, and make decisions. Previous research studies have shown that nearly 60 million Americans experienced a mental illness in 2024, yet there were only 340 people for every one mental health provider in the U.S. Furthermore, young adults aged 18–25—who are the most digitally connected generation—suffer from the highest rates of severe mental illness yet are the least likely to seek or receive treatment. These findings highlight a growing crisis where more people are struggling with mental health issues, but the resources available to help them remain insufficient. This study presents a comprehensive investigation into predictive models and datasets for early mental health intervention, combining a systematic literature review with empirical research. We examine a range of existing machine learning algorithms and datasets that focus on behavioral and physiological indicators, including heart rate variability, sleep patterns, device usage, and social interaction metrics. Through critical evaluation of these models, we identify key features and data types most effective for predicting early signs of mental health conditions. Based on these insights, we design a predictive system architecture, including form-matching tables that align symptom inputs with appropriate risk levels and recommended actions. To translate the system into an accessible user experience, we develop mobile application wireframes and conduct usability research on features that support early detection and intervention. This work aims to bridge the gap between technical innovation and user-centered design, offering a holistic and proactive approach to empowering mental wellness through early intervention.
Included in
UR-099 Empowering Mental Wellness: A Comprehensive Study and Design of a Predictive System for Early Mental Health Intervention
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
Mental health is an essential part of living a balanced and fulfilling life, but it is often overlooked compared to physical health. While physical health is important for performing daily activities, mental health plays a crucial role in how we manage stress, build connections, and make decisions. Previous research studies have shown that nearly 60 million Americans experienced a mental illness in 2024, yet there were only 340 people for every one mental health provider in the U.S. Furthermore, young adults aged 18–25—who are the most digitally connected generation—suffer from the highest rates of severe mental illness yet are the least likely to seek or receive treatment. These findings highlight a growing crisis where more people are struggling with mental health issues, but the resources available to help them remain insufficient. This study presents a comprehensive investigation into predictive models and datasets for early mental health intervention, combining a systematic literature review with empirical research. We examine a range of existing machine learning algorithms and datasets that focus on behavioral and physiological indicators, including heart rate variability, sleep patterns, device usage, and social interaction metrics. Through critical evaluation of these models, we identify key features and data types most effective for predicting early signs of mental health conditions. Based on these insights, we design a predictive system architecture, including form-matching tables that align symptom inputs with appropriate risk levels and recommended actions. To translate the system into an accessible user experience, we develop mobile application wireframes and conduct usability research on features that support early detection and intervention. This work aims to bridge the gap between technical innovation and user-centered design, offering a holistic and proactive approach to empowering mental wellness through early intervention.
https://digitalcommons.kennesaw.edu/cday/Spring_2025/Undergraduate_Research/11
