[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119233-en":3,"doc-seo-119233-105":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},119233,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Utilizing machine learning to predict employee turnover in high-stress sectors - Research report","This study applies machine learning to predict employee turnover in high-stress sectors, aiming to strengthen retention by detecting potential turnover risk with higher accuracy. A comprehensive dataset is used, incorporating employee demographics, job satisfaction, performance metrics, and stress levels. Predictive modeling compares logistic regression, decision trees, random forests, and neural networks, supported by preprocessing, feature selection, cross-validation, and hyper-parameter tuning. Results evaluate accuracy, precision, recall, and AUC-ROC, showing random forests and neural networks as top performers. Key turnover drivers include job satisfaction, stress, and performance ratings.","OPEN ACCESS  \nInternational Journal of Management & Entrepreneurship Research P-ISSN: 2664-3588, E-ISSN: 2664-3596  \nVolume 6, Issue 5, P.No.1702-1732, May 2024  \nDOI: 10.51594/ijmer.v6i5.1143  \nFair East Publishers [Journal Homepage: ](Journal Homepage: www.fepbl.com/index.php/ijmer)[www.fepbl.com/index.php/ijmer](Journal Homepage: www.fepbl.com/index.php/ijmer)  \nUtilizing machine learning to predict employee turnover in high  \nstress sectors  \nKudirat Bukola Adeusi 1, Prisca Amajuoyi2, & Lucky Bamidele Benjami3  \n1Communications Software (Airline Systems) limited a member of Aspire Software Inc, UK  \n2Independent Researcher, UK  \n3Independent Researcher, London, UK  \nCorresponding Author: Kudirat Bukola Adeusi  \n[Corresponding Author Email: ](Corresponding Author Email: bukolakadeusi@gmail.com)[bukolakadeusi@gmail.com](Corresponding Author Email: bukolakadeusi@gmail.com)  \nArticle Received: 25-01-24 Accepted: 05-04-24 Published: 20-05-24  \nLicensing Details: Author retains the right of this article. The article is distributed under the terms of  \nthe Creative Commons Attribution-Non Commercial 4.0 License  \n([http://www.creativecommons.org/licences/by-nc/4.0/](http://www.creativecommons.org/licences/by-nc/4.0/)), which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page.  \nABSTRACT  \nThis study investigates the application of machine learning techniques to predict employee turnover in high-stress sectors. The primary objective is to enhance retention strategies by accurately identifying potential turnover risks. The research utilizes a comprehensive dataset comprising various factors, including employee demographics, job satisfaction, performance metrics, and stress levels. Multiple machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, are employed to build predictive models. The methodology involves data preprocessing, feature selection, model training, and evaluation. Cross-validation and hyper parameter tuning are performed to ensure the robustness and accuracy of the models. The performance of each algorithm is assessed using metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC) .  \nKey findings reveal that machine learning models can effectively predict employee turnover, with random forests and neural networks demonstrating superior performance. Significant predictors of turnover include job satisfaction, stress levels, and performance ratings. The study concludes that integrating machine learning models into human resource practices can  \nprovide valuable insights for preemptive interventions, ultimately reducing turnover rates in high-stress environments.  \nFuture research should explore the integration of real-time data and the potential of deep learning techniques to further enhance predictive accuracy. Additionally, the ethical implications of using predictive models in HR decisions warrant careful consideration to ensure fairness and transparency.  \nKeywords: Machine Learning (ML), Employee Turnover, Predictive Analytics, Human Resources (HR), High-Stress Sectors, Decision Trees, Random Forests, Extreme Gradient Boosting (XGBoost), Personalized Retention Strategies, Business Intelligence (BI) Tools, Data Quality, Ethical Considerations, Data Privacy, Natural Language Processing (NLP), Deep Learning, Real-time Data Analysis, Employee Engagement, Work-Life Balance, Organizational Performance, Data-Driven Insights.  \nINTRODUCTION  \nImportance of Predicting Employee Turnover  \nPredicting employee turnover is crucial for organizations to maintain stability and sustain productivity. High turnover rates can result in substantial financial losses, decreased morale, and disrupted organizational performance (Mhatre et al., 2020) . By proactively identifying employees at risk","cbCaipIH5VJIRILS","https://ap.wps.com/l/cbCaipIH5VJIRILS","pdf",679403,1,31,"English","en",105,"# Abstract\n# Introduction\n## Importance of predicting employee turnover\n## Machine learning approaches for turnover prediction\n# Methodology (from abstract)\n## Data preprocessing and feature selection\n## Model training and evaluation\n# Findings (from abstract)\n## Predictive performance comparison\n## Key predictors of turnover\n# Conclusion and future work (from abstract)\n## Real-time data and deep learning\n## Ethical and transparency considerations","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To use machine learning to accurately identify employees at risk of turnover in high-stress sectors, enabling proactive retention interventions.\"},{\"question\":\"Which data factors are included to build the turnover prediction models?\",\"answer\":\"The study uses employee demographics, job satisfaction, performance metrics, and stress levels.\"},{\"question\":\"Which machine learning methods perform best, and what predicts turnover most strongly?\",\"answer\":\"Random forests and neural networks show superior performance, while job satisfaction, stress levels, and performance ratings are highlighted as significant predictors.\"}]","Utilizing machine learning to predict employee turnover in high-stress sectors - 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