[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126054-en":3,"doc-seo-126054-105":29,"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":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126054,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Machine learning classification meets migraine - recommendations for study evaluation","Machine learning (ML) classification techniques are increasingly applied in migraine research, providing new perspectives on the pathophysiology and on migraine types and subtypes. Key limitations—heterogeneous study design, insufficient methodological transparency, and lack of external validation—reduce impact and reproducibility. The paper proposes six essential recommendations to evaluate ML-based classification studies, addressing cohort homogeneity, sample size, data quality control, transparent model evaluation, clinically relevant interpretability, and open data/code sharing to support robust generalization.","Petrušić et al. The Journal of Headache and Pain (2024) 25:215  \n[https://doi.org/10.1186/s10194-024-01924-x](https://doi.org/10.1186/s10194-024-01924-x)  \nThe Journal of Headache and Pain  \nMETHODOLOGY Open Access  \nMachine learning classification meets migraine: recommendations for study evaluation  \nIgor Petrušić 1*, A. Andrej Savić2, Katarina Mitrović3, Nebojša Bačanin4, Gabriele Sebastianel li5, Daniele Secci6 and Gianluca Coppola5  \nAbstract  \nThe integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of external validation limit the impact and reproducibility of such studies. This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility. These recommendations aim to balance the trade-off between model generalization and precision while encouraging collaborative standardization across the ML and headache communities. Furthermore, this framework intends to stimulate discussion toward forming a consortium to establish definitive guidelines for ML-based classification research in migraine field.  \nKeywords Benchmark, Machine learning classification models, Data quality, Model interpretability, Model reproducibility, Migraine types  \n*Correspondence:  \nIgor Petrušić  \n[ip7med@yahoo.com](ip7med@yahoo.com)  \n1Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, Belgrade, Serbia  \n2Science and Research Centre, School of Electrical Engineering, University of Belgrade, University of Belgrade, Belgrade, Serbia  \n3Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac, Čačak, Serbia  \n4Department of Informatics and Computing, Singidunum University, Belgrade, Serbia  \n5Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino ICOT, Latina, Italy  \n6Department of Engineering and Architecture, University of Parma, Parma, Italy  \nIntroduction  \nIn recent years, the emerging use of machine learning (ML) classification techniques in headache research has led to promising new understandings about different migraine types and subtypes [1]. ML is a branch of artificial intelligence focused on implementing computational algorithms that enable the recognition of patterns and relationships within data, achieving improved performance through learning and adaptation based on experience [2]. Unlike traditional statistical methods, ML emphasizes pattern recognition and predictive modeling through algorithms uncovering novel insights from intricate datasets [3]. ML comprises a range of task types, including classification, regression, clustering,  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included","cbCais08Zz3NT7MB","https://ap.wps.com/l/cbCais08Zz3NT7MB","pdf",960688,1,"English","en",105,"# Abstract\n# Introduction\n## Background: ML in headache research\n## Rationale for standardized evaluation","[{\"question\":\"What problems limit the current impact of ML-based migraine classification studies?\",\"answer\":\"Inconsistencies in study design, limited methodological transparency, and the absence of external validation restrict both impact and reproducibility.\"},{\"question\":\"What are the six key recommendations proposed for evaluating ML-based classification in migraine research?\",\"answer\":\"They cover group homogenization, adequate sample size, data quality control, transparent training/testing and performance evaluation, interpretability with clinical relevance, and open data and code sharing.\"},{\"question\":\"How do the recommendations balance model generalization and precision?\",\"answer\":\"They aim to manage the trade-off between generalization and precision while encouraging shared standardization efforts across ML and headache research communities.\"}]","Machine learning classification meets migraine - recommendations for study evaluation | PDF",1785902809,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"machine-learning-classification-meets-migraine-recommendations-for-study-evaluation","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-classification-meets-migraine-recommendations-for-study-evaluation/126054/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-21","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":11},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problems limit the current impact of ML-based migraine classification studies?","Question",{"text":75,"@type":76},"Inconsistencies in study design, limited methodological transparency, and the absence of external validation restrict both impact and reproducibility.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the six key recommendations proposed for evaluating ML-based classification in migraine research?",{"text":80,"@type":76},"They cover group homogenization, adequate sample size, data quality control, transparent training/testing and performance evaluation, interpretability with clinical relevance, and open data and code sharing.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the recommendations balance model generalization and precision?",{"text":84,"@type":76},"They aim to manage the trade-off between generalization and precision while encouraging shared standardization efforts across ML and headache research communities.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"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":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":45,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]