Ostach, Mary Ann
(2024)
Exploring Machine Learning Methods to Estimate Effects of Aging and Physical Activity Levels in the Dog Aging Project.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
This study follows a One Health approach in examining the relationship between physical activity measures and aging through analysis of data from the Dog Aging Project with companion animal dogs as the subject model. Physical activity outcomes include lifestyle, intensity, and average daily total active time. In addition to linear regression and multinomial logistic regression, machine learning methods including random forest and extreme gradient boosting are implemented. This study found that the machine learning methods can provide slightly higher predictive abilities with key explanatory variables of dog age, environment type, dog weight, and owner age being highly ranked in importance.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
18 December 2024 |
| Date Type: |
Publication |
| Defense Date: |
9 December 2024 |
| Approval Date: |
18 December 2024 |
| Submission Date: |
12 December 2024 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
113 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Machine Learning. Dog Aging Project. |
| Date Deposited: |
18 Dec 2024 19:49 |
| Last Modified: |
18 Dec 2024 19:49 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/47248 |
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