[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122563-en":3,"doc-seo-122563-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122563,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning Algorithms for Detecting Mental Stress in College Students","Stress is a major concern impacting the health and well-being of college students, often leading to serious physical and psychological outcomes. This study forecasts stress versus non-stress occurrences by applying multiple machine learning algorithms, including Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and k-Nearest Neighbors. A workshop of approximately 843 students (18–21 years) provided a validated questionnaire dataset covering emotional, physical, academic, relationship, and leisure dimensions. Results show Support Vector Machines achieving up to 95% accuracy for stress detection.","Machine Learning Algorithms for Detecting Mental  \nStress in College Students  \nAshutosh Singh†, Khushdeep Singh, Amit Kumar, Abhishek Shrivastava , and Santosh Kumar  \n[ashutoshs22102@iiitnr.edu.in](ashutoshs22102@iiitnr.edu.in), [khushdeep22102@iiitnr.edu.in](khushdeep22102@iiitnr.edu.in), [amit22102@iiitnr.edu.in](amit22102@iiitnr.edu.in),  \n[abhisheks@iiitnr.edu.in](abhisheks@iiitnr.edu.in) and [santosh@iiitnr.edu.in](santosh@iiitnr.edu.in)  \nDepartment of Data Science and Artificial Intelligence  \nIIIT Naya Raipur, Chhattisgarh, India 493661  \narXiv :2412 .07415v1 [ cs .LG] 10 Dec 2024  \nAbstract—In today’s world, stress is a big problem that affects people’s health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and Knearest Neighbors. The primary objective of this work is to leverage a research study to predict and mitigate stress and nonstress based on the collected questionnaire dataset. We conducted a workshop with the primary goal of studying the stress levels found among the students. This workshop was attended by Approximately 843 students aged between 18 to 21 years old. A questionnaire was given to the students validated under the guidance of the experts from the All India Institute of Medical Sciences (AIIMS) Raipur, Chhattisgarh, India, on which our dataset is based. The survey consists of 28 questions, aiming to comprehensively understand the multidimensional aspects of stress, including emotional well-being, physical health, academic performance, relationships, and leisure. This work finds that Support Vector Machines have a maximum accuracy for Stress, reaching 95% . The study contributes to a deeper understanding of stress determinants. It aims to improve college student’s overall quality of life and academic success, addressing the multifaceted nature of stress.  \nIndex Terms—Stress, Non-stress, Machine learning algorithms, Support vector machines  \nI. INTRODUCTION  \nThe college journey is an adventure of growth and selfdiscovery, challenging students to expand their minds and develop their character. It provides countless opportunities for personal and academic advancement, yet it also presents formidable obstacles that can profoundly impact students well-being. Recognizing and addressing these challenges is essential to creating a supportive and empowering college experience that meets the diverse needs of each individual 1. Stress detection encompasses various categories, including acute stress, chronic stress, episodic acute stress, eustress, and distress. Acute stress stems from short-term physiological changes triggered by specific events or pressures, while chronic stress persists due to ongoing conflicts or issues.  \n1[https://www.usnews.com/education/best-colleges/articles/](https://www.usnews.com/education/best-colleges/articles/)[ ](https://www.usnews.com/education/best-colleges/articles/)[stress-in-college-students-what-to-know](stress-in-college-students-what-to-know)  \nEpisodic acute stress manifests as repeated instances of acute stress, often affecting individuals with chaotic lifestyles. Understanding the complexities of stress, both positive and negative, is crucial as it affects not only individuals but society as a whole [1] and [3] .  \nThe emergence of the COVID-19 pandemic has brought into focus the widespread prevalence of stress and anxiety among students, as corroborated by recent research findings. A study conducted by The Center of Healing (TCOH) in India [9] revealed a stark increase in stress and anxiety levels among a sample of over 10,000 respondents, underscoring the magnitude of the issue [2] .  \nThe two distinct","cbCaidxZFO34Drxn","https://ap.wps.com/l/cbCaidxZFO34Drxn","pdf",402467,1,5,"English","en",105,"# Introduction\n## Stress detection categories\n## Motivation and approach\n# Related Work\n## Wrist-based monitoring and deep learning","[{\"question\":\"Which machine learning algorithms are used for stress vs. non-stress classification?\",\"answer\":\"The study evaluates Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and k-Nearest Neighbors.\"},{\"question\":\"How is the dataset collected and what does the questionnaire cover?\",\"answer\":\"A workshop with about 843 students (ages 18–21) collected responses to a validated questionnaire consisting of 28 questions covering emotional well-being, physical health, academic performance, relationships, and leisure.\"},{\"question\":\"What model delivers the best accuracy for detecting stress?\",\"answer\":\"Support Vector Machines achieves the maximum reported accuracy for stress detection, reaching 95%.\"}]","Machine Learning Algorithms for Detecting Mental Stress in College Students | 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machine learning algorithms are used for stress vs. non-stress classification?","Question",{"text":75,"@type":76},"The study evaluates Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and k-Nearest Neighbors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset collected and what does the questionnaire cover?",{"text":80,"@type":76},"A workshop with about 843 students (ages 18–21) collected responses to a validated questionnaire consisting of 28 questions covering emotional well-being, physical health, academic performance, relationships, and leisure.",{"name":82,"@type":73,"acceptedAnswer":83},"What model delivers the best accuracy for detecting stress?",{"text":84,"@type":76},"Support Vector Machines achieves the maximum reported accuracy for stress detection, reaching 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