Abstract
An existing StudentLife Study mobile dataset was evaluated and organized to be applied to different machine learning methods. Different variables like user activity, exercise, sleep, study space, social, and stress levels are optimized to train a model that could predict user stress level. The different machine learning methods would test if both patient data privacy and training efficiency can be ensured.
Faculty Advisor/Mentor
Jiangwen Sun
Document Type
Paper
Disciplines
Artificial Intelligence and Robotics | Digital Communications and Networking | Information Security
DOI
10.25776/n58k-h415
Publication Date
11-2021
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Included in
Artificial Intelligence and Robotics Commons, Digital Communications and Networking Commons, Information Security Commons
Protection of Patient Privacy on Mobile Device Machine Learning
An existing StudentLife Study mobile dataset was evaluated and organized to be applied to different machine learning methods. Different variables like user activity, exercise, sleep, study space, social, and stress levels are optimized to train a model that could predict user stress level. The different machine learning methods would test if both patient data privacy and training efficiency can be ensured.