Research on Students’ Mental Health Based on Data Mining Algorithms

Luo, Meiming

doi:10.1155/2021/1382559

Cited by 6 publications

(6 citation statements)

References 47 publications

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“…One of the key ways to stop students from experiencing a psychological crisis is to develop a system for managing their W psychological data. Many scientists are attempting to apply machine learning to anticipate complicated psychological problems that people may experience, such as anticipating stress and anxiety disorders [7]. Only the traits that are most accurate in predicting suicide risk can reach thousands, according to studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The suicide of Weibo users is assessed in actual time using a multilayer perceptron algorithm built specifically for the social media site. The forecast accuracy percentage might be as high as 94% in terms of possibility [7]. Luo [7] created an initial data collection using the user's response outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The forecast accuracy percentage might be as high as 94% in terms of possibility [7]. Luo [7] created an initial data collection using the user's response outcomes. A psychological exam data set to be mined is generated after pre-processing information exchange, extraction, cleaning, and conversion.…”

Section: Literature Reviewmentioning

confidence: 99%

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Preventing Student’s Mental Health Problems with the Help of Data Mining

Hossain,

Fahad,

Ahmed

et al. 2024

IJC

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The increasing incidence of mental health issues among university students has become a significant concern, often referred to as a "mental health crisis" in academic settings. This study addresses the challenge of predicting mental health issues in university students using data mining techniques. The research involved the creation of a new dataset via a survey method focused on university students, covering various factors like behavioral traits, health conditions, and lifestyle choices. Data mining algorithms such as Naive Bayes, Random Forest, SVM, KNN, and Decision Tree were employed to predict mental health status. The study included dataset collection, cleaning, integration, transformation, reduction, discretization, and the use of Weka and Orange for data analysis. Therefore, exploratory analysis revealed that 53.4% of students reported depression, with a higher incidence among male students and those less involved in extracurricular activities. Predictive analysis showed Naive Bayes as the most accurate algorithm (65.91%) for this prediction task, followed by Random Forest, SVM, KNN, and Decision Tree. The performance was evaluated using accuracy, F1-Score, precision, recall, AUC, and CA. The study highlights the correlation between various aspects of university students' lives and depression. Active participation in extracurricular activities was found to lower depression risks. The effectiveness of data mining in understanding and predicting mental health issues in university students was established, with Naive Bayes being the most effective algorithm for this purpose.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Preventing Student’s Mental Health Problems with the Help of Data Mining

Hossain,

Fahad,

Ahmed

et al. 2024

IJC

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“…The results showed that the prediction accuracy was 96.98% [36]. In addition, many researchers have attempted to use neural networks to predict a person's mental problems, such as stress and anxiety disorders [37]. Further, MLPNN was used to assess the likelihood of suicide using real-time user data on Weibo.…”

Section: Multi-layer Perceptron Neural Networkmentioning

confidence: 99%

“…The goals of psychology are to understand, predict, and manage behavior [18]. Nowadays, the majority of psychology studies concentrate on describing the relationships between variables [19]. However, research that truly achieves predictable goals is not uncommon.…”

Section: Introductionmentioning

confidence: 99%

Improving the Avoidant Personality Disorder Prediction for Higher Education Using SMOTE-ENN and Multi-Layer Perceptron Neural Network

Nuanmeesri

Poomhiran

2023

TEM Journal

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Adolescents in higher education are more prone to Avoidant Personality Disorder (AVPD), which strongly affects academic achievement. The goal of this study was to create models for accurate prediction of the likelihood of Avoidant Personality Disorder among students in higher education. Information Gain, Gain Ratio, and Wrapper Approach are used as feature selection methods combined with data resampling techniques and machine learning, including Multi-Layer Perceptron Neural Network, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine. The findings revealed that the Wrapper approach gave higher accuracy than Information Gain and Gain Ratio approach. Further, using the Gain Ratio approach gives the model a slightly higher efficiency than the Information Gain. Furthermore, when comparing feature selection and data resampling, it was found that the model using feature selection had more higher model efficiency than data resampling alone. Additionally, combining the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbor (SMOTE-ENN) considerably increased the model’s effectiveness. Finally, the model’s efficiency was at its maximum, with an accuracy of 95.52%, when the Wrapper approach was used in conjunction with the Synthetic Minority Over-sampling Technique, the Edited Nearest Neighbor algorithm, and the Multi-Layer Perceptron Neural Network.

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Mental Health Prediction Among Students Using Machine Learning Techniques

Sahu

Debbarma

2023

Smart Innovation, Systems and Technologies

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Research on Students’ Mental Health Based on Data Mining Algorithms

Cited by 6 publications

References 47 publications

Preventing Student’s Mental Health Problems with the Help of Data Mining

Preventing Student’s Mental Health Problems with the Help of Data Mining

Improving the Avoidant Personality Disorder Prediction for Higher Education Using SMOTE-ENN and Multi-Layer Perceptron Neural Network

Mental Health Prediction Among Students Using Machine Learning Techniques

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