Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
Caregiver burnout is a significant issue in healthcare delivery and management, as it directly impacts caregivers' health and compromises the standard of care, often leading to negligence, health deterioration, or withdrawal from caregiving duties. Caregivers play a crucial role in supporting the health, well-being, and quality of life of care recipients by providing both personal and professional services. However, the continuous needs and stress associated with caregiving duties can affect their health and everyday life, leading to caregiver burnout. This study applied data analytics and machine learning by merging several feature selection methods on the NHATS dataset, including LightGBM, XGBoost, and RFE with Random Forest, to identify the critical features contributing to caregiver burnout. Furthermore, utilizing the selected crucial features enables the separation of low and high caregiver burnout cases geometrically using a linear SVM, with points near the decision margin indicating individuals with early signs of burnout. We also developed a healthcare modeling and simulation approach that incorporates a scheduling optimization strategy, balancing caregiver workloads, and early interventions to mitigate severe burnout risks. This strategy will revolutionize the way real-world challenges of caregiving are addressed, with the potential to enhance caregiver well-being.
Included in
GRM-0204 Unpacking Early Burnout through Predictive Risk Boundaries
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Caregiver burnout is a significant issue in healthcare delivery and management, as it directly impacts caregivers' health and compromises the standard of care, often leading to negligence, health deterioration, or withdrawal from caregiving duties. Caregivers play a crucial role in supporting the health, well-being, and quality of life of care recipients by providing both personal and professional services. However, the continuous needs and stress associated with caregiving duties can affect their health and everyday life, leading to caregiver burnout. This study applied data analytics and machine learning by merging several feature selection methods on the NHATS dataset, including LightGBM, XGBoost, and RFE with Random Forest, to identify the critical features contributing to caregiver burnout. Furthermore, utilizing the selected crucial features enables the separation of low and high caregiver burnout cases geometrically using a linear SVM, with points near the decision margin indicating individuals with early signs of burnout. We also developed a healthcare modeling and simulation approach that incorporates a scheduling optimization strategy, balancing caregiver workloads, and early interventions to mitigate severe burnout risks. This strategy will revolutionize the way real-world challenges of caregiving are addressed, with the potential to enhance caregiver well-being.