[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124025-en":3,"doc-seo-124025-105":30,"detail-sidebar-cat-0-en-105":83},{"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},124025,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning analysis of patients’ perceptions towards generic medication in Greece - a survey-based study","Survey-based research examines Greek patients’ perceptions and attitudes toward generic drugs, targeting the identification of factors that drive acceptance and market penetration of generics in Greece. Despite recognized cost-saving potential, patient skepticism is treated as a major adoption barrier. A mixed-methods design analyzes responses from 2,617 adults using descriptive statistics and machine learning models, with hyperparameter tuning to predict willingness to switch.","TYPE Original Research PUBLISHED 20 March 2024  \nDOI 10.3389/fdsfr.2024.1363794  \nOPEN ACCESS  \nEDITED BY  \nAssaf Gottlieb,  \nUniversity of Texas Health Science Center at Houston, United States  \nREVIEWED BY  \nSalvatore Crisafulli, University of Verona, Italy Anna Staniszewska,  \nMedical University of Warsaw, Poland  \n*CORRESPONDENCE  \nChristos Kontogiorgis,  [ckontogi@med.duth.gr](ckontogi@med.duth.gr)  \nRECEIVED 31 December 2023  \nACCEPTED 05 March 2024  \nPUBLISHED 20 March 2024  \nCITATION  \nKassandros K, Saranti E, Misailidou E, Tsiggou T-A, Sissiou E, Kolios G, Constantinides T and Kontogiorgis C (2024), Machine learning analysis of patients ’perceptions towards generic medication in Greece: a survey-based study.  \nFront. Drug Saf. Regul. 4:1363794 .  \ndoi: 10.3389/fdsfr.2024.1363794  \nCOPYRIGHT  \n© 2024 Kassandros, Saranti, Misailidou, Tsiggou, Sissiou, Kolios, Constantinides and Kontogiorgis. This is an open-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.  \nMachine learning analysis of patients’ perceptions towards generic medication in Greece: a survey-based study  \nKonstantinos Kassandros 1, Evridiki Saranti 1, Evropi Misailidou 1,  \nTheodora-Aiketerini Tsiggou 1, Eleftheria Sissiou 1, George Kolios 2, Theodoros Constantinides 1 and Christos Kontogiorgis 1*  \n1Laboratory of Hygiene and Environmental Protection, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece, 2Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece  \nIntroduction: This survey-based study investigates Greek patients’ perceptionsand attitudes towards generic drugs, aiming to identify factors influencing the acceptance and market penetration of generics in Greece. Despite the acknowledged cost-saving potential of generic medication, skepticism among patients remains a barrier to their widespread adoption.  \nMethods: Between February 2017 and June 2021, a mixed-methods approach was employed, combining descriptive statistics with advanced machine learning models (Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and XGBoost) to analyze responses from 2,617 adult participants. The study focused on optimizing these models through extensive hyperparameter tuning to predict patient willingness to switch to a generic medication.  \nResults: The analysis revealed healthcare providers as the primary information source about generics for patients. Significant differences in perceptions were observed across demographic groups, with machine learning models successfully identifying key predictors for the acceptance of generic drugs, including patient knowledge and healthcare professional influence. The Random Forest model demonstrated the highest accuracy and was selected as the most suitable for this dataset.  \nDiscussion: The findings underscore the critical role of informed healthcare providers in influencing patient attitudes towards generics. Despite the study ’s focus on Greece, the insights have broader implications for enhancing generic drug acceptance globally. Limitations include reliance on convenience sampling and self-reported data, suggesting caution in generalizing results.  \nKEYWORDS  \ngeneric medication, Greece, machine learning, healthcare, patients’ perception, questionnaire  \nFrontiers in Drug Safety and Regulation 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nGeneric medicines have worldwide acceptance and utilization due to their benefit on both patients and healthcare systems (Dunne, 2016; Arcaro et al., 2021) . In many countries, such as Greece, whi","cbCainQT0eCq5GZH","https://ap.wps.com/l/cbCainQT0eCq5GZH","pdf",1329111,1,12,"English","en",105,"# Introduction\n## Legislative context and adoption barriers\n# Methods\n## Study design and machine learning models\n# Results\n## Key predictors and model performance\n# Discussion\n## Provider influence, implications, and limitations","[{\"question\":\"Which factors and findings were most strongly linked to acceptance of generics?\",\"answer\":\"Healthcare providers were identified as the primary information source about generics. Patient knowledge and healthcare professional influence were key predictors, with the Random Forest model showing the highest accuracy.\"}]","Machine learning analysis of patients’ perceptions towards generic medication in Greece - a survey-based study | PDF",1785819930,30,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-analysis-of-patients-perceptions-towards-generic-medication-in-greece-a-survey-based-study","",{"@graph":36,"@context":77},[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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-analysis-of-patients-perceptions-towards-generic-medication-in-greece-a-survey-based-study/124025/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Which factors and findings were most strongly linked to acceptance of generics?","Question",{"text":75,"@type":76},"Healthcare providers were identified as the primary information source about generics. 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