Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression
Depression is a mental illness influenced by various factors, including stress in everyday life, physical activities, and physical diseases. It accompanies such symptoms as continuous depression, sleep disorder, and suicide attempts. In the healthcare, it is necessary to predict diverse situations a...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8964291/ |
| _version_ | 1818664331261247488 |
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| author | Ji-Won Baek Kyungyong Chung |
| author_facet | Ji-Won Baek Kyungyong Chung |
| author_sort | Ji-Won Baek |
| collection | DOAJ |
| description | Depression is a mental illness influenced by various factors, including stress in everyday life, physical activities, and physical diseases. It accompanies such symptoms as continuous depression, sleep disorder, and suicide attempts. In the healthcare, it is necessary to predict diverse situations accurately. Accordingly, in order to care for mental health, it is necessary to recognize individuals' situations and continue to manage them. In the area of mental diseases and treatment, research has been conducted to find a patient's state with the use of big data and to monitor the worst situation. Mental illnesses typically have depression. Research on Mental healthcare using artificial intelligence do conduct on prediction based on patients' voice, word choice, and conversation length. However, there is not much research on situation prediction in order to prevent depression. Therefore, this study proposes the context-DNN model for predicting depression risk using multiple-regression. The context of the proposed context-DNN consists of the information to predict situations and environments influencing depression in consideration of context information. Each context information related to predictor variables of depression becomes an input of DNN, and variable for depression prediction becomes an output of DNN. For DNN connection, the regression analysis to predict the risk of depression is used so as to predict the potential context influencing the risk of depression. According to the performance evaluation, the proposed model was evaluated to have the best performance in regression analysis and comparative analysis with DNN. |
| first_indexed | 2024-12-17T05:31:02Z |
| format | Article |
| id | doaj.art-2e5d3aecf323461093178e286ab0cce3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:31:02Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2e5d3aecf323461093178e286ab0cce32022-12-21T22:01:43ZengIEEEIEEE Access2169-35362020-01-018181711818110.1109/ACCESS.2020.29683938964291Context Deep Neural Network Model for Predicting Depression Risk Using Multiple RegressionJi-Won Baek0Kyungyong Chung1https://orcid.org/0000-0002-6439-9992Department of Computer Science, Kyonggi University, Suwon-si, South KoreaDivision of Computer Science and Engineering, Kyonggi University, Suwon-si, South KoreaDepression is a mental illness influenced by various factors, including stress in everyday life, physical activities, and physical diseases. It accompanies such symptoms as continuous depression, sleep disorder, and suicide attempts. In the healthcare, it is necessary to predict diverse situations accurately. Accordingly, in order to care for mental health, it is necessary to recognize individuals' situations and continue to manage them. In the area of mental diseases and treatment, research has been conducted to find a patient's state with the use of big data and to monitor the worst situation. Mental illnesses typically have depression. Research on Mental healthcare using artificial intelligence do conduct on prediction based on patients' voice, word choice, and conversation length. However, there is not much research on situation prediction in order to prevent depression. Therefore, this study proposes the context-DNN model for predicting depression risk using multiple-regression. The context of the proposed context-DNN consists of the information to predict situations and environments influencing depression in consideration of context information. Each context information related to predictor variables of depression becomes an input of DNN, and variable for depression prediction becomes an output of DNN. For DNN connection, the regression analysis to predict the risk of depression is used so as to predict the potential context influencing the risk of depression. According to the performance evaluation, the proposed model was evaluated to have the best performance in regression analysis and comparative analysis with DNN.https://ieeexplore.ieee.org/document/8964291/Deep neural networkcontextdepression riskmental healthmultiple regressionhealthcare |
| spellingShingle | Ji-Won Baek Kyungyong Chung Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression IEEE Access Deep neural network context depression risk mental health multiple regression healthcare |
| title | Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression |
| title_full | Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression |
| title_fullStr | Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression |
| title_full_unstemmed | Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression |
| title_short | Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression |
| title_sort | context deep neural network model for predicting depression risk using multiple regression |
| topic | Deep neural network context depression risk mental health multiple regression healthcare |
| url | https://ieeexplore.ieee.org/document/8964291/ |
| work_keys_str_mv | AT jiwonbaek contextdeepneuralnetworkmodelforpredictingdepressionriskusingmultipleregression AT kyungyongchung contextdeepneuralnetworkmodelforpredictingdepressionriskusingmultipleregression |