[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127364-en":3,"doc-seo-127364-105":30,"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":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},127364,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",7,"Healthcare","A machine learning web application for screening social anxiety disorder based on participants’ emotion regulation (ML-SAD)","Social Anxiety Disorder (SAD) is an often-overlooked condition that limits timely diagnosis and access to expert evaluation. To address this gap, the study presents a machine learning–based web application for self-screening using emotion regulation. The system includes 10 multimedia scenarios to evaluate users’ coping competence across specific social situations via three emotion regulation strategies. In a Persian-speaking sample of 488 young adults (18–35), participants were labeled SAD or non-SAD based on diagnostic history and self-rated anxiety. Multiple models were trained and achieved over 80% accuracy, supporting reliable identification of individuals needing further support.","TYPE Original Research PUBLISHED 25 September 2025 DOI 10.3389/frobt.2025.1620609  \nOPEN ACCESS  \nEDITED BY  \nSebastian Schneider,  \nUniversity of Twente, Netherlands  \nREVIEWED BY  \nKaushik Pratim Das, Christ University, India Dilshan De Silva,  \nSri Lanka Institute of Information Technology, Sri Lanka  \n*CORRESPONDENCE  \nSara Ahmadi Majd,  \n [sara.ahmadimajd@gmail.com](sara.ahmadimajd@gmail.com)  \nRECEIVED 13 May 2025  \nACCEPTED 29 August 2025  \nPUBLISHED 25 September 2025  \nCITATION  \nAhmadi Majd S, Parsaeian MR, Madani M, Moradi H and Mohammadi A (2025) A machine learning web application for screening social anxiety disorder based on participants’ emotion regulation (ML-SAD) . Front. Robot. AI 12:1620609.  \ndoi: 10.3389/frobt.2025.1620609  \nCOPYRIGHT  \n© 2025 Ahmadi Majd, Parsaeian, Madani, Moradi and Mohammadi. This is an  \nopen-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA machine learning web application for screening social anxiety disorder based on participants’ emotion regulation (ML-SAD)  \nSara Ahmadi Majd 1,2*, Mohamad Rasoul Parsaeian 2,  \nMohsen Madani 3, Hadi Moradi 3 and Abolfazl Mohammadi 4  \n1Campus Institute Data Science, Georg-August-Universität Göttingen, Göttingen, Germany, 2College of Psychology and Educational Sciences, University of Tehran, Tehran, Iran, 3College of Electrical and Computer Engineering, University of Tehran, Tehran, Iran, 4 Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran  \nSocial Anxiety Disorder (SAD) is called a neglected anxiety disorder since people do not realize its existence and the need to receive further treatment. Thus, it is essential to develop widely available self-screening systems to assess individuals and direct those who need further evaluation to appropriate resources. Consequently, this paper presents a web application based on machine learning to screen for SAD. The Web application comprises 10 multimedia scenarios that people with SAD may struggle with. Four hundred and eighty-eight young adults (18–35 years old) in Persian-speaking society were asked to consider themselves in these scenarios and rank their competency in dealing with each specific situation, considering three emotion regulation strategies. Participants were divided into two groups, SAD and non-SAD, based on their diagnostic history of SAD and their self-assessment of their anxiety level. Multiple machine learning models were trained and evaluated, achieving an accuracy rate of more than 80% and demonstrating the effectiveness of the tool in identifying individuals who need additional support.  \nKEYWORDS  \nsocial anxiety disorder, emotion regulation, machine learning, web application, screening tools  \n1 Introduction  \nSocial Anxiety Disorder (SAD) is a persistent and intense fear of a social situation in which the individual believes that they may be humiliated, embarrassed, or negatively judged (Association and Association, 2013) . SAD may disrupt all aspects of a person’s life, with problems in education, work, and personal relationships. For example, people with SAD have been shown to have higher unemployment and reduced marriage rates compared to normal people (Wittchen et al., 2000) . The high prevalence of this disorder, that is, about 12% of the population, has led it to be fifth among psychiatric disorders (Alonso et al., 2004). Unfortunately, like most psychiatric disorders, the diagnosis methods of SAD are based on interviews and clinical assessments (Nordgaard et al., 2012) . Consequently, this results in the limited availability of these methods, especially in rural  \nFront","cbCaiul2McDfWbZp","https://ap.wps.com/l/cbCaiul2McDfWbZp","pdf",18656760,1,11,"English","en",105,"# Introduction\n## Web-based self-screening for SAD\n## Multimedia scenario design and emotion regulation strategies\n## Machine learning screening and evaluation","[{\"question\":\"What problem does the ML-SAD web application address?\",\"answer\":\"It provides widely accessible self-screening for social anxiety disorder, reducing reliance on limited expert interviews and clinical assessments.\"},{\"question\":\"How does the application measure emotion regulation in social situations?\",\"answer\":\"It presents 10 multimedia scenarios and asks users to rank their competency in dealing with each situation using three emotion regulation strategies.\"},{\"question\":\"How well did the machine learning models perform?\",\"answer\":\"Trained and evaluated models reached an accuracy rate above 80%, indicating effectiveness in identifying people who may need additional support.\"}]","A machine learning web application for screening social anxiety disorder based on participants’ emotion regulation (ML-SAD) | 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problem does the ML-SAD web application address?","Question",{"text":76,"@type":77},"It provides widely accessible self-screening for social anxiety disorder, reducing reliance on limited expert interviews and clinical assessments.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the application measure emotion regulation in social situations?",{"text":81,"@type":77},"It presents 10 multimedia scenarios and asks users to rank their competency in dealing with each situation using three emotion regulation strategies.",{"name":83,"@type":74,"acceptedAnswer":84},"How well did the machine learning models perform?",{"text":85,"@type":77},"Trained and evaluated models reached an accuracy rate above 80%, indicating effectiveness in identifying people who may need additional 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