[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126338-en":3,"doc-seo-126338-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126338,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Exploring Emotional Stability - From Conventional Approaches to Machine Learning Insights","Contemporary psychological assessment often relies on extensive questionnaires to evaluate diverse traits. This study targets emotional stability and addresses limitations tied to long survey instruments by proposing a machine learning approach for direct prediction. Variables were selected based on previously established relationships and evaluated on data from 2203 individuals who completed psychometric questionnaires. The method achieves an R2 near 0.71 on the test set, highlighting emotional stress and self-esteem as key predictors, with comparable performance using reduced feature sets.","Applied Intelligence  \nExploring Emotional Stability: From Conventional Approaches to Machine Learning  \nInsights  \n--Manuscript Draft--  \n\n| Manuscript Number: | APIN-D-24-03623R2 |\n| --- | --- |\n| Full Title: | Exploring Emotional Stability: From Conventional Approaches to Machine Learning Insights |\n| Article Type: | Original Submission |\n| Keywords: | Machine Learning; data mining, emotional stability, psychology |\n| Corresponding Author: | Luis Gonzaga Baca Ruiz\u003Cbr>University of Granada: Universidad de Granada Granada, Spain / Andalucía / Granada SPAIN |\n| Corresponding Author Secondary Information: |  |\n| Corresponding Author's Institution: | University of Granada: Universidad de Granada |\n| Corresponding Author's Secondary Institution: |  |\n| First Author: | Marcos Romero Madroñal |\n| First Author Secondary Information: |  |\n| Order of Authors: | Marcos Romero Madroñal |\n|  | Eduar S. Ramirez |\n|  | Luis Gonzaga Baca Ruiz |\n|  | María José Serrano Fernández |\n|  | Elena Pérez Moreiras |\n|  | María del Carmen Pegalajar Jiménez |\n| Order of Authors Secondary Information: |  |\n| Funding Information: |  |\n| Abstract: | In contemporary psychological assessments, diverse traits are often evaluated using extensive questionnaires. This study focuses on the trait of emotional stability, and acknowledges the inherent limitations and issues associated with prolonged survey instruments. To address these challenges, we propose a Machine Learning (ML) approach to directly predict emotional stability, offering a more efficient alternative to bulky questionnaires. The study carefully selected variables with previously established relationships to emotional stability, utilizing a dataset of 2203 individuals who responded to a series of psychometric questionnaires. The proposed method yields promising results, achieving an R2 score of approximately 0.71 on the test set, indicating robust predictive performance. These models highlighted the significance of variables such as emotional stress and self-esteem, emphasizing their substantial role in predicting emotional stability. It is noteworthy that even with a reduced set of variables, the models remained statistically equivalent. The results provide valuable insights for predicting stability with smaller sets of variables and contribute knowledge that complements the understanding of emotional stability. |\n\nPowered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation  \nResponse to Reviewer Comments  \nWe thank the reviewers for their work during this process.  \nAll typos commented by the reviewers have been corrected. No more modifications were mentioned.  \nManuscript Click here to view linked References   \n1  \n2  \n3  \n4  \n5  \n6  \n7  \n8  \n9  \n10  \n11  \n12  \n13  \n14  \n15  \n16  \n17  \n18  \n19  \n20  \n21  \n22  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34  \n35  \n36  \n37  \n38  \n39  \n40  \n41  \n42  \n43  \n44  \n45  \n46  \n47  \n48  \n49  \n50  \n51  \n52  \n53  \n54  \n55  \n56  \n57  \n58  \n59  \n60  \n61  \n62  \n63  \n64  \nExploring Emotional Stability: From Conventional Approaches to Machine Learning Insights  \nMarcos Romero Madroñal 1, Eduar S. Ramírez2, Luis Gonzaga Baca Ruiz3, María José Serrano-Fernández4, Elena Pérez-Moreiras5, María del Carmen Pegalajar Jiménez1,*  \n1Department of Computer Science and Artificial Intelligence, University of Granada, Granada, 18014, Andalucía, Spain; [e.maromad@go.ugr.es](e.maromad@go.ugr.es), [mcarmen@decsai.ugr.es](mcarmen@decsai.ugr.es)[ ](mcarmen@decsai.ugr.es)2Faculty of Health Sciences, Universidad Villanueva, Calle Costa Brava 2, 28034 Madrid, Spain; eduar.ramirez@villanueva.edu  \n3Department of Software Engineering, University of Granada, Granada, 18014, Andalucía, Spain; [bacaruiz@ugr.es](bacaruiz@ugr.es)  \n4Department of Psychology, Rovira I Virgili University, 43002 Tarragona, Spain; [mariajose.serrano@urv.cat](mariajose.serrano@urv.cat)  \n5RH Asesores Improving S.L., Instituto para el Desarrollo del Talento Natural y el Nuevo L","cbCaiqx2ao7sVMUc","https://ap.wps.com/l/cbCaiqx2ao7sVMUc","pdf",1153481,4,1,28,"English","en",105,"# Introduction\n## Emotional stability and neuroticism background\n## Motivation: limitations of conventional questionnaires\n## Proposed machine learning approach\n## Data and variable selection\n## Results and predictive performance\n## Key predictors and reduced-variable analysis","[{\"question\":\"Why does the study move from questionnaires to machine learning for emotional stability?\",\"answer\":\"Prolonged questionnaires create practical limitations, so the study proposes machine learning to predict emotional stability more efficiently while maintaining strong performance.\"},{\"question\":\"What dataset and population size are used to train and test the models?\",\"answer\":\"The study uses responses from 2203 individuals who completed psychometric questionnaires.\"},{\"question\":\"Which variables are identified as important for predicting emotional stability?\",\"answer\":\"Emotional stress and self-esteem are emphasized as significant variables contributing to the prediction of emotional stability.\"}]","Exploring Emotional Stability - From Conventional Approaches to Machine Learning Insights | PDF",1785904548,71,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"exploring-emotional-stability-from-conventional-approaches-to-machine-learning-insights","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/exploring-emotional-stability-from-conventional-approaches-to-machine-learning-insights/126338/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does the study move from questionnaires to machine learning for emotional stability?","Question",{"text":76,"@type":77},"Prolonged questionnaires create practical limitations, so the study proposes machine learning to predict emotional stability more efficiently while maintaining strong performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset and population size are used to train and test the models?",{"text":81,"@type":77},"The study uses responses from 2203 individuals who completed psychometric questionnaires.",{"name":83,"@type":74,"acceptedAnswer":84},"Which variables are identified as important for predicting emotional stability?",{"text":85,"@type":77},"Emotional stress and self-esteem are emphasized as significant variables contributing to the prediction of emotional stability.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]