[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122106-en":3,"doc-seo-122106-105":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},122106,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","A Machine Learning Approach to Analyze Manpower Sleep Disorder - Predictive modeling and insights","Human resources determine workplace efficiency, while sleep disorders can reduce workforce productivity and increase fatigue-related costs. This study uses data analytics to examine physical and medical-related features of manpower and builds an accurate predictive framework for sleep disorder types. Machine learning with metaheuristic optimization (eXtreme Gradient Boosting and particle swarm optimization) is implemented in Python with Scikit-learn, evaluated via accuracy, recall, precision, and F1-score. Results reach 93.1% accuracy and reveal associations between occupation, BMI, and specific disorders.","OriginalArticle:  \nA Machine Learning Approach to Analyze Manpower Sleep Disorder  \nReza Amiri1* , Mohammad Noori1  \n1. Faculty of Science and New Technologies, Islamic Azad University, Medical Science Branch, Shariati St, Tehran, Iran  \nCite this article as: Amiri, R., & Noori, M. (2024). A Machine Learning Approach to Analyze the Manpower Sleep Disorder and Fatigue Problems. Archives of Advances in Biosciences, 15(1), 1–11. [https://doi.org/10.22037/aab.v15i1.44853](https://doi.org/10.22037/aab.v15i1.44853)  \nI  \n[https://journals.sbmu.ac.ir/aab/article/view/44853](https://journals.sbmu.ac.ir/aab/article/view/44853)  \n1. Introduction  \nn the fast-paced landscape of contemporary workplaces, the significance of adequate sleep in the realm of manpower has emerged as a critical consideration. Sleep, a  \nfundamental pillar of well-being, not only affects an individual's health but also plays a crucial role in shaping workplace dynamics and organizational outcomes. Exhaustion exerts a significant economic toll, causing employers to incur billions of dollars in expenses annually [1] . It is approximated that declines  \nin both productivity and motivation, coupled with healthcare expenditures linked to fatigue, result in an average cost of $1,967 per employee for individual employers each year [1] . Aggregating these productivity losses, workplace fatigue collectively amounts to approximately $136.4 billion annually for companies in the United States [2]. Therefore, this paper delves into the intricate interplay between sleeping issues and manpower, and identifies the effective factors, which can influence employee efficiency and overall performance, with the help of artificial intelligence methods. Thus, this will guide decision-  \nArchives of Advances  \nin Biosciences  \n\n| \u003Cbr>Article info:\u003Cbr>Received: 10 Mar 2024\u003Cbr>Accepted: 28 Sep 2024\u003Cbr>Published: 01 Oct 2024\u003Cbr>* Corresponding author:\u003Cbr>Reza Amiri, MD.\u003Cbr>Address: Faculty of Science and New Technologies, Islamic Azad University, Medical Science Branch, Shariati St, Tehran, Iran\u003Cbr>E-mail:\u003Cbr>[behnamamiri.r@gmail.com](behnamamiri.r@gmail.com) | Abstract\u003Cbr>Introduction: Human resources play a pivotal role in determining the efficiency of a workplace and an organization. One major issue that significantly influences workforce productivity is sleep disorders. Machine learning can be applied to predict sleep disorders and analyze how various factors, such as lifestyle and environmental conditions, contribute to the development of these disorders, paving the way for more effective interventions and solutions.\u003Cbr>Materials and Methods: In this research, by utilizing data analytic methods, some physical and medical-related features of manpower are investigated to make beneficial observations. Moreover, a combination of machine learning and metaheuristic algorithms such as eXtreme Gradient Boosting and particle swarm optimization are used to make an accurate predictive model. Also, the accuracy, recall, precision, and F1-score metrics are utilized to evaluate the model. The Python and Scikit-learn package are used to analyze the problem and implement algorithms.\u003Cbr>Results: The outcome is a predictive model with 93.1% accuracy to predict the type of sleep disorder and some useful insights like the relationship of different variables like job and physical characteristics with the sleep disorder. It is observed that one’s occupation has the most impact on insomnia (1.25) and BMI has the most effect on sleep apnea (1) .\u003Cbr>Conclusion: The implementation of a predictive model helps identify existing issues and enables proactive measures to prevent potential problems, allowing decision-makers to design targeted interventions and wellness programs. Continuous monitoring and adjustments based on the model’s predictions ensure adaptive strategies that improve employee health and workplace efficiency, fostering a resilient workforce and enhancing overall organizational performance.\u003Cbr>Keyw","cbCaih27aTAwqV59","https://ap.wps.com/l/cbCaih27aTAwqV59","pdf",1710812,1,11,"English","en",105,"# Introduction\n# Materials and Methods\n## Data and Feature Analysis\n## Machine Learning and Metaheuristics\n## Evaluation Metrics\n# Results\n# Conclusion","[{\"question\":\"What is the main goal of the machine learning study on sleep disorders?\",\"answer\":\"To build a predictive model that identifies the type of sleep disorder and analyzes how manpower-related factors contribute to sleep issues.\"},{\"question\":\"Which algorithms and optimization methods are used to create the prediction model?\",\"answer\":\"The approach combines machine learning with metaheuristic algorithms, including eXtreme Gradient Boosting and particle swarm optimization.\"},{\"question\":\"What performance and key findings does the model achieve?\",\"answer\":\"The model reaches 93.1% accuracy and provides insights such as occupation having the most impact on insomnia and BMI having the most effect on sleep apnea.\"}]","A Machine Learning Approach to Analyze Manpower Sleep Disorder - Predictive modeling and insights | PDF",1785808847,28,{"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},"a-machine-learning-approach-to-analyze-manpower-sleep-disorder-predictive-modeling-and-insights","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-to-analyze-manpower-sleep-disorder-predictive-modeling-and-insights/122106/",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","2026-08-04",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 goal of the machine learning study on sleep disorders?","Question",{"text":75,"@type":76},"To build a predictive model that identifies the type of sleep disorder and analyzes how manpower-related factors contribute to sleep issues.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which algorithms and optimization methods are used to create the prediction model?",{"text":80,"@type":76},"The approach combines machine learning with metaheuristic algorithms, including eXtreme Gradient Boosting and particle swarm optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance and key findings does the model achieve?",{"text":84,"@type":76},"The model reaches 93.1% accuracy and provides insights such as occupation having the most impact on insomnia and BMI having the most effect on sleep apnea.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]