Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring.
10.4258/hir.2017.23.4.285
- Author:
Se Hui SONG
1
;
Dong Keun KIM
Author Information
1. Department of Mobile Software, Graduate School, Sangmyung University, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
Stress;
Stress Classification Model;
Deep Belief Network;
Machine Learning;
KNHANES
- MeSH:
Classification*;
Dataset;
Delivery of Health Care;
Forests;
Korea;
Learning;
Machine Learning;
Methods;
Nutrition Surveys;
Quality of Life;
Sensitivity and Specificity;
Support Vector Machine
- From:Healthcare Informatics Research
2017;23(4):285-292
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: Stress management is related to public healthcare and quality of life; an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. METHODS: In this paper, we present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data. Statistical analysis was performed to identify the nine features of stress detection, and we evaluated the performance of the proposed stress classification by comparison with several stress detection models. The proposed model was also evaluated using Deep Belief Networks (DBN). RESULTS: We designed profiles depending on the number of hidden layers, nodes, and hyper-parameters according to the loss function results. The experimental results showed that the proposed model achieved an accuracy and a specificity of 66.23% and 75.32%, respectively. The proposed DBN model performed better than other classification models, such as support vector machine, naive Bayesian classifier, and random forest. CONCLUSIONS: The proposed model in this study was demonstrated to be effective in classifying stress detection, and in particular, it is expected to be applicable for stress prediction in stress monitoring systems.
