Summary of Machine Learning Algorithms For Detecting Mental Stress in College Students, by Ashutosh Singh et al.
Machine Learning Algorithms for Detecting Mental Stress in College Students
by Ashutosh Singh, Khushdeep Singh, Amit Kumar, Abhishek Shrivastava, Santosh Kumar
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models are being applied to predict and mitigate stress among college students. Researchers used various algorithms, including Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and K-nearest Neighbors, to forecast stress and non-stress occurrences based on a questionnaire dataset collected from approximately 843 students aged 18-21 years old. The study found that Support Vector Machines had the highest accuracy for predicting Stress, reaching 95%. The work aims to contribute to a deeper understanding of stress determinants and improve college students’ overall quality of life and academic success. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stress is a big problem that affects people’s health and happiness. Many people feel stressed out, which can lead to serious health issues. A new study used machine learning models to try to predict and stop stress among college students. The researchers asked over 800 students questions about their feelings, physical health, academic performance, relationships, and free time. They then used different algorithms to see if they could forecast when the students would feel stressed or not. One type of algorithm called Support Vector Machines did very well, correctly predicting stress 95% of the time. The study hopes to help college students have a better life and do well in school by understanding more about what causes stress. |
Keywords
» Artificial intelligence » Logistic regression » Machine learning » Naive bayes » Random forest
