Prediction and Analysis of Contemporary College Students’ Mental Health Based on Neural Network

Pei, Jingjing

doi:10.1155/2022/7284197

Cited by 3 publications

(1 citation statement)

References 15 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, new artificial intelligence algorithms represented by deep learning have developed rapidly. From the reported results, deep learning models can dig deeper into the internal structure of data and obtain more reliable results than traditional machine learning algorithms [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Mental Health Education in Primary and Secondary Schools Based on Deep Learning

Wang¹,

An²

2022

Journal of Sensors

View full text Add to dashboard Cite

Strengthening the mental health education of primary and middle school students is a key measure to lead their healthy growth. This paper takes mental health early warning as the starting point to carry out research on mental health education in primary and secondary schools. Aiming at the defect that the existing methods cannot effectively warn the mental health of primary and secondary school students, this paper proposes a mental health early warning research method based on the deep learning models. The method firstly obtains the mental health data of college students through the symptom self-rating scale. Then, a mental health prediction model combining convolutional neural networks (CNN), deep residual networks (ResNet), and long short-term memory (LSTM) is adopted. Through in-depth processing and analysis of mental health data, the mental health status of students can be determined. Experiments show that this method can effectively improve the accuracy of mental health early warning compared with the currently commonly used mental health early warning methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Mental Health Education in Primary and Secondary Schools Based on Deep Learning

Wang¹,

An²

2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

Media dependency, platform-swinging, and psychological depression among young college students in the polymedia environment

Wangqu,

Xue,

Chen

2024

Sci Rep

View full text Add to dashboard Cite

As a comprehensive and affordable environment, polymedia lays the groundwork for exploring platform-swinging and offers a theoretical framework for investigating its dynamics. Platform-swinging and its unique niche characteristics provide a novel lens to examine the interplay between media dependency and psychological well-being. Using a survey questionnaire, this study collected data from 1210 university students in the Yangtze River Delta, China’s most economically developed region, to examine the relationship between media dependency, platform-swinging, and psychological depression. Our findings suggest that the affordances of platform-swinging influence media dependency, whereas the nature of media dependency shapes psychological depression. This study contributes to a deeper understanding of how young adults navigate various media platforms and sheds light on the complex interrelations between media use and mental health. Focusing on platform-swinging in the environment of polymedia explores the relationship between media dependency, platform-swinging, and psychological depression. Moreover, it also compensates for the one-sided understanding caused by simply examining media dependency and psychological depression. This study has practical value and theoretical significance for the psychological health of college students and the research and development of the media dependency theory.

show abstract

Evaluating The Predictive Reliability of Neural Networks in Psychological Research With Random Datasets

Cheng,

Petrides

2024

Educational and Psychological Measurement

View full text Add to dashboard Cite

Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship between ordinal independent variables and continuous or binary-dependent variables) can provide an acceptable level of predictive performance from a psychologist’s perspective. Through a Monte Carlo simulation study, we found that this kind of erroneous conclusion is not likely to be drawn as long as the sample size is larger than 50 with continuous-dependent variables. However, when the dependent variable is binary, the minimum sample size is 500 when the criteria are balanced accuracy ≥ .6 or balanced accuracy ≥ .65, and the minimum sample size is 200 when the criterion is balanced accuracy ≥ .7 for a decision error less than .05. In the case where area under the curve (AUC) is used as a metric, a sample size of 100, 200, and 500 is necessary when the minimum acceptable performance level is set at AUC ≥ .7, AUC ≥ .65, and AUC ≥ .6, respectively. The results found by this study can be used for sample size planning for psychologists who wish to apply neural networks for a qualitatively reliable conclusion. Further directions and limitations of the study are also discussed.

show abstract

Prediction and Analysis of Contemporary College Students’ Mental Health Based on Neural Network

Cited by 3 publications

References 15 publications

Mental Health Education in Primary and Secondary Schools Based on Deep Learning

Mental Health Education in Primary and Secondary Schools Based on Deep Learning

Media dependency, platform-swinging, and psychological depression among young college students in the polymedia environment

Evaluating The Predictive Reliability of Neural Networks in Psychological Research With Random Datasets

Contact Info

Product

Resources

About