Prediction of University Students’ Subjective Well-Being with Sleep and Physical Activity Data using Classification Algorithms

Daily activities affect mental health. One of the most used scales is "subjective well-being (SWB)", which is a self-reported questionnaire. This study aimed to predict SWBs using step count, heart rate and sleep duration data from sensors instead of questionnaires. NetHealth data from the...

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Bibliographic Details
Published in:Procedia computer science 2022, Vol.207, p.2648-2657
Main Authors: Kılıç, Akif Can, Karakuş, Ahmet, Alptekin, Emre
Format: Article
Language:English
Subjects:
classification
machine learning
physical activity
sleep
Subjective well-being
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Summary:Daily activities affect mental health. One of the most used scales is "subjective well-being (SWB)", which is a self-reported questionnaire. This study aimed to predict SWBs using step count, heart rate and sleep duration data from sensors instead of questionnaires. NetHealth data from the University of Notre Dame1 has been used. Attributes included average daily steps, average heart rate, heartbeat standard deviation, average sleep duration, and sleep duration deviation. Preprocessing, processing, classification, and evaluation followed. Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Ensemble classifiers were used. Performance metrics include accuracy, precision, recall, F1-Score, and ROC (Receiver Operating Characteristic) curves. Model accuracy was 62%. This indicates that machine learning could be beneficial in detecting SWB levels using sensor data.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2022.09.323