Advisor(s)
Dr. Kropp
Confirmation
1
Document Type
Poster
Location
ONU McIntosh Center; Activities Room
Start Date
11-4-2025 10:00 AM
End Date
11-4-2025 10:50 AM
Abstract
Ohio Northern University’s Mobile Health Clinic sets up in various locations in the surrounding communities to provide vital services to areas with limited access to health resources. However, scheduling and coordinating visits to the local communities is complicated, and it is hard to choose which locations at which times will provide the most benefit to the local communities.
In our research, a multiobjective machine learning algorithm is utilized to balance the various factors involved in setting up the mobile health clinic in various communities to determine where the clinic can be set up so that it can have the most impact. The objectives are defined in code and the algorithm is instructed to minimize or maximize each of the objectives, while avoiding adversely affecting the other objectives. Some of the objectives include minimizing distance or cost, maximizing the service area or people able to access the clinic, and maximizing the number of locations served.
The machine learning algorithm should help schedule locations for the mobile health clinic to visit so that it can be more effective in providing service while reducing the work required for someone to try to calculate the best route manually.
Recommended Citation
Freer, Samuel and Spinner, Elijah, "Mobile Health Clinic Research" (2025). ONU Student Research Colloquium. 26.
https://digitalcommons.onu.edu/student_research_colloquium/2025/Posters/26
Restricted
Available to ONU community via local IP address and ONU login.
Mobile Health Clinic Research
ONU McIntosh Center; Activities Room
Ohio Northern University’s Mobile Health Clinic sets up in various locations in the surrounding communities to provide vital services to areas with limited access to health resources. However, scheduling and coordinating visits to the local communities is complicated, and it is hard to choose which locations at which times will provide the most benefit to the local communities.
In our research, a multiobjective machine learning algorithm is utilized to balance the various factors involved in setting up the mobile health clinic in various communities to determine where the clinic can be set up so that it can have the most impact. The objectives are defined in code and the algorithm is instructed to minimize or maximize each of the objectives, while avoiding adversely affecting the other objectives. Some of the objectives include minimizing distance or cost, maximizing the service area or people able to access the clinic, and maximizing the number of locations served.
The machine learning algorithm should help schedule locations for the mobile health clinic to visit so that it can be more effective in providing service while reducing the work required for someone to try to calculate the best route manually.