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Healthc Inform Res. 2017 Oct;23(4):285-292. English. Original Article. https://doi.org/10.4258/hir.2017.23.4.285
Song SH , Kim DK .
Department of Mobile Software, Graduate School, Sangmyung University, Seoul, Korea.
Department of Intelligent Engineering Informatics for Human, College of Convergence Engineering, Sangmyung University, Seoul, Korea. dkim@smu.ac.kr
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.

Copyright © 2019. Korean Association of Medical Journal Editors.