[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124915-en":3,"doc-seo-124915-105":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":4,"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},124915,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Predictive Modelling Of Stress Levels - A Comparative Analysis Of Machine Learning Algorithms","This research paper investigates the efficacy of multiple machine learning algorithms for predicting stress levels. Using a dataset to train and test candidate models, the study compares algorithm performance with metrics such as accuracy, precision, recall, and F1 score. The work aims to determine the most accurate and reliable approach for stress prediction and to support development of more effective stress prediction models. Potential applications include healthcare screening, workplace wellness, and improved personal well-being through timely and actionable insights.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 45 IssueS-4 Year 2024 Page 153-158  \nPredictive Modelling Of Stress Levels: A Comparative Analysis Of Machine  \nLearning Algorithms  \nAsst. Prof. Sumit Sasane1*, Dr. Zameer Ahmed S Mulla2  \n1*Indira College of Commerce and Science, Pune. Email: [sumit.sasane@iccs.ac.in](sumit.sasane@iccs.ac.in)  \n2D. Y. Patil College, [Lohgaon. Email: zsmulla63@gmail.com](Lohgaon. Email: zsmulla63@gmail.com)  \n*Corresponding Author: Asst. Prof. Sumit Sasane  \nEmail: [sumit.sasane@iccs.ac.in](sumit.sasane@iccs.ac.in)  \n\n| CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>This research paper investigates the efficacy of various machine learning algorithms in predicting stress levels. By employing a diverse set of algorithms, including [List of Algorithms], we aim to identify the most accurate and reliable model for stress prediction. The study utilizes [Dataset Information] to train and test the algorithms, evaluating their performance based on metrics such as accuracy, precision, recall, and F1 score. The findings will contribute to the development of more effective stress prediction models, with potential applications in healthcare, workplace wellness, and personal well-being.\u003Cbr>Keyword: Stress Prediction, Machine Learning Algorithms, Predictive Modelling, Comparative Analysis. |\n| --- | --- |\n\n1. Introduction  \nIn today's fast-paced and demanding world, stress has become a pervasive concern affecting individuals across various domains of life. Despite the growing awareness of stress-related issues, predicting and managing stress levels remains a complex task, often relying on subjective assessments or limited predictive models. This research seeks to enhance the accuracy and reliability of stress prediction by conducting a comparative analysis of various machine learning algorithms, ultimately identifying the most effective approach for predicting stress levels. The findings ofthis study have the potential to revolutionize stress prediction, providing more accurate and timely insights that could inform personalized interventions, improve mental health outcomes, and contribute to a better understanding of the factors influencing stress levels. To achieve our research objectives, we conducted a comprehensive comparative analysis of machine learning algorithms, including [list of algorithms], using a dataset sourced from [describe the dataset] . This approach allowed us to evaluate the performance of each algorithm in predicting stress levels. In the following sections, we delve into the methodology, present our findings, and discuss the implications of our research. By the conclusion, we aim to provide valuable insights into the selection of machine learning algorithms for accurate stress prediction.  \n1.1 Commonly used algorithms for predictive modelling of stress levels:  \nThe machine learning algorithms for stress prediction depends on various factors, including the nature of the data, the complexity of the problem, and the desired interpretability of the model. Here are some commonly used algorithms for predictive modelling of stress levels:  \nSupport Vector Machines (SVM): SVMs are effective for classification tasks, and they work well in scenarios with clear class boundaries. They can be suitable for stress prediction when the data has distinct patterns. Random Forest: Random Forest is an ensemble learning algorithm that combines multiple decision trees to improve accuracy and robustness. It can handle non-linear relationships and interactions in the data, making ita good candidate for stress prediction.  \nLogistic Regression: Despite its simplicity, logistic regression can be effective for binary classification tasks. It provides a clear interpretation of feature importance and is computationally efficient.  \nK-Nearest Neighbors (KNN): KNN is a non-parametric algorithm that classifies data points based on the majority class of their neighbors. It can be suitable for stress prediction if t","cbCaiteEeVXLRuvr","https://ap.wps.com/l/cbCaiteEeVXLRuvr","pdf",375143,1,6,"English","en",105,"# Introduction\n## Commonly used algorithms for predictive modelling of stress levels\n# Research Significance","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"The study aims to improve the accuracy and reliability of stress prediction by conducting a comparative analysis of machine learning algorithms.\"},{\"question\":\"How are the machine learning algorithms evaluated?\",\"answer\":\"Algorithms are trained and tested using a dataset, and their performance is assessed using metrics such as accuracy, precision, recall, and F1 score.\"},{\"question\":\"Which kinds of algorithms are commonly considered for stress level prediction?\",\"answer\":\"The paper discusses options including Support Vector Machines, Random Forest, Logistic Regression, KNN, Decision Trees, Gradient Boosting methods (e.g., XGBoost/LightGBM), and Neural Networks.\"}]","Predictive Modelling Of Stress Levels - A Comparative Analysis Of Machine Learning Algorithms | PDF",1785895368,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predictive-modelling-of-stress-levels-a-comparative-analysis-of-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predictive-modelling-of-stress-levels-a-comparative-analysis-of-machine-learning-algorithms/124915/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"The study aims to improve the accuracy and reliability of stress prediction by conducting a comparative analysis of machine learning algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the machine learning algorithms evaluated?",{"text":80,"@type":76},"Algorithms are trained and tested using a dataset, and their performance is assessed using metrics such as accuracy, precision, recall, and F1 score.",{"name":82,"@type":73,"acceptedAnswer":83},"Which kinds of algorithms are commonly considered for stress level prediction?",{"text":84,"@type":76},"The paper discusses options including Support Vector Machines, Random Forest, Logistic Regression, KNN, Decision Trees, Gradient Boosting methods (e.g., XGBoost/LightGBM), and Neural Networks.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]