Evaluation Model of the Mental Health Education Effectiveness Based on Deep Neural Networks
This research develops a deep neural network model called DNN-MHE to evaluate mental health education effects. A questionnaire survey collected data on 916 students' mental health knowledge, attitudes, and behaviors. DNN-MHE uses five fully connected layers to predict mental health metrics. Exp...
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| Veröffentlicht in: | Journal of computing and information technology 2023-03, Vol.31 (1), p.57-72 |
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| container_title | Journal of computing and information technology |
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| creator | Luo, Junmei Deng, Shuchao |
| description | This research develops a deep neural network model called DNN-MHE to evaluate mental health education effects. A questionnaire survey collected data on 916 students' mental health knowledge, attitudes, and behaviors. DNN-MHE uses five fully connected layers to predict mental health metrics. Experiments demonstrate that DNN-MHE achieves 99.46% accuracy, outperforming RNN, CNN, and shallow MLP models. Ablation studies validate the impact of training iterations, number of neurons, and number of data samples on performance. Overall, DNN-MHE enables accurate and efficient analysis of mental health education with practical implications for improving university programs. |
| doi_str_mv | 10.20532/cit.2023.1005727 |
| format | Article |
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| language | eng |
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| source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Analysis Data mining deep neural network, mental health education, neuron numbers Health aspects Health education Health surveys Machine learning Medical personnel Mental health Neural networks Neurons Study and teaching Surveys Training |
| title | Evaluation Model of the Mental Health Education Effectiveness Based on Deep Neural Networks |
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