Machine Learning Based Evaluations of Stress, Depression, and Anxiety

Trivedi, Naresh Kumar; Tiwari, Raj Gaurang; Witarsyah, Deden; Gautam, Vinay; Misra, Alok; Nugraha, Ryan Adhitya

doi:10.1109/icadeis56544.2022.10037336

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…These classifications are determined based on specific score ranges. In the case of depression, the five levels are categorized as normal (0-9), mild (10)(11)(12)(13), moderate (14)(15)(16)(17)(18)(19)(20), severe (21)(22)(23)(24)(25)(26)(27), and extremely severe (≥28). Similarly, for anxiety, the levels are categorized as normal (0-7), mild (8)(9), moderate (10)(11)(12)(13)(14), severe (15)(16)(17)(18)(19), and extremely severe (≥20).…”

Section: Classified Output Of Depression Anxiety and Stressmentioning

confidence: 99%

“…Similarly, for anxiety, the levels are categorized as normal (0-7), mild (8)(9), moderate (10)(11)(12)(13)(14), severe (15)(16)(17)(18)(19), and extremely severe (≥20). In the context of stress, the five levels are categorized as normal (0-14), mild (15)(16)(17)(18), moderate (19)(20)(21)(22)(23)(24)(25), severe (26)(27)(28)(29)(30)(31)(32)(33), and extremely severe (≥34) [50].…”

Section: Classified Output Of Depression Anxiety and Stressmentioning

confidence: 99%

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An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

Tuan,

Trang,

et al. 2024

J. Hum. Earth Future

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Objectives: This study explores different machine learning models (KNN: k-nearest neighbor, MLP: Multilayer Perceptron, SVM: Support Vector Machine) to identify the optimal model for accurate and rapid mental health detection among the recovered COVID-19 patients. Other techniques are also investigated, such as feature selection (Recursive Feature Elimination (RFE) and Extra Trees (ET) methods) and hyper-parameter tuning, to achieve a system that could effectively and quickly indicate mental health. Method/Analysis: To achieve the objectives, the study employs a dataset collected from recovered COVID-19 patients, encompassing information related to depression, anxiety, and stress. Machine learning models are utilized in the analysis. Additionally, feature selection methods and hyper-parameter tuning techniques are explored to enhance the model’s predictive capabilities. The performance of each model is assessed based on accuracy metrics. Findings: The experimental results show that SVM is the most suitable model for accurately predicting an individual’s mental health among recovered COVID-19 patients (accuracy ≥ 0.984). Furthermore, the ET method is more effective than the RFE method for feature selection in the anxiety and stress datasets. Novelty/Improvement:The study lies in the understanding of predictive modeling for mental health and provides insights into the choice of models and techniques for accurate and early detection. Doi: 10.28991/HEF-2024-05-01-01 Full Text: PDF

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Section: Classified Output Of Depression Anxiety and Stressmentioning

confidence: 99%

Section: Classified Output Of Depression Anxiety and Stressmentioning

confidence: 99%

An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

Tuan,

Trang,

et al. 2024

J. Hum. Earth Future

View full text Add to dashboard Cite

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“…Naresh Kumar et al compares the classification effects of Decision Tree algorithm, K-NNN algorithm, Naive Bayes algorithm, Random Forest Tree algorithm and Support Vector Machine algorithm [20]. Adrienne, G. et al used support vector machine-radial basis function (SVMRBF), Random Forest (RF) and logistic regression (LR) algorithms for classification [21].…”

Section: Classification Algorithmmentioning

confidence: 99%

A Stacking Model-Based Classification Algorithm Is Used to Predict Social Phobia

Li,

Xu,

Chen

et al. 2024

Applied Sciences

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University students, as a special group, face multiple psychological pressures and challenges, making them susceptible to social anxiety disorder. However, there are currently no articles using machine learning algorithms to identify predictors of social anxiety disorder in university students. This study aims to use a stacked ensemble model to predict social anxiety disorder in university students and compare it with other machine learning models to demonstrate the effectiveness of the proposed model. AUC and F1 are used as classification evaluation metrics. The experimental results show that in this dataset, the model combining logistic regression, Naive Bayes, and KNN algorithms as the first layer and Naive Bayes as the second layer performs better than traditional machine learning algorithms. This provides a new approach to studying social anxiety disorder.

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SMS Spam Detection Using Deep Learning Techniques: A Comparative Analysis of DNN Vs LSTM Vs Bi-LSTM

Gandhi

Sarangi

Saxena

et al. 2023

2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)

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Machine Learning Based Evaluations of Stress, Depression, and Anxiety

Cited by 11 publications

References 17 publications

An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

A Stacking Model-Based Classification Algorithm Is Used to Predict Social Phobia

SMS Spam Detection Using Deep Learning Techniques: A Comparative Analysis of DNN Vs LSTM Vs Bi-LSTM

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