[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290184-105":3,"doc-detail-290184-en":81,"detail-sidebar-cat-0-en-105":99},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","comparative-analysis-of-web-based-machine-learning-models","Comparative analysis of web-based machine learning models","","This paper presents a comparative analysis of web-based machine learning models, focusing on Google Vertex AI, Google Teachable Machine, Azure Machine Learning, and Salesforce Einstein Vision. The study evaluates their suitability for integration into a medical information system as a medical-image classification module. Comparison criteria include model accuracy, ease of integration, and scalability, with an emphasis on improving medical image classification capabilities. Results are intended to support selection of an optimal solution for healthcare decision support.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/comparative-analysis-of-web-based-machine-learning-models/290184/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/comparative-analysis-of-web-based-machine-learning-models/290184.png","ImageObject",300,407,{"name":42,"@type":43},"Himbo","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Which web-based machine learning platforms are compared in the paper?","Question",{"text":63,"@type":64},"Google Vertex AI, Google Teachable Machine, Azure Machine Learning, and Salesforce Einstein Vision are compared as candidates for a medical-image classification module.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What is the main objective of the comparative evaluation?",{"text":68,"@type":64},"To assess the platforms’ suitability for integration into a medical information system, specifically as a classification module for medical images.",{"name":70,"@type":61,"acceptedAnswer":71},"What criteria are used to compare the models?",{"text":72,"@type":64},"The evaluation considers model accuracy, ease of integration, and scalability, and also selects based on performance measures such as F1 score and adaptability across medical image databases.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290184,1790413177,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":97,"read_time":98,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Comparative analysis of web-based machine  \nlearning models  \nAna-Maria ȘTEFAN, Elena OVREIU, Mihai CIUC  \nFaculty of Electronics, Telecommunication, and Information Technology, National University of Science and Technology Politehnica Bucharest, Romania  \n[ana_maria.stefan@stud.fim.upb.ro](ana_maria.stefan@stud.fim.upb.ro), [elena.ovreiu@upb.ro](elena.ovreiu@upb.ro), [mihai.ciuc@upb.ro](mihai.ciuc@upb.ro)  \nAbstract: This paper presents a comparative analysis of web-based machine learning models, specifically examining Google Vertex AI, Google Teachable Machine, Azure Machine Learning and Salesforce Einstein Vision. The objective is to assess their suitability for integration into a medical information system as a classification module for medical images. The comparative evaluation considers factors such as model accuracy, ease of integration and scalability. The findings aim to guide the selection of an optimal machine learning solution for enhancing the capabilities of medical image classification within a healthcare context.  \nKeywords: Healthcare, Web-based Machine Learning Models, Decision Support, Classification Module, Medical Image Analysis.  \nAnaliza comparativă a modelelor de învățare web-based  \nRezumat: Acest articol prezintă o analiză comparativă a modelelor de învățare automată web-based, examinând în mod specific Google Vertex AI, Google Teachable Machine, Azure Machine Learning și Salesforce Einstein Vision. Scopul este de evalua aceste sisteme în vederea integrării acestora într-un sisteminformatic medical ca modul de clasificare pentru imagini medicale. Evaluarea comparativă ia în considerare factori precum acuratețea modelului, ușurința de integrare și scalabilitatea. Concluziile urmăresc să ghideze selecția unei soluții optime de învățare automată pentru îmbunătățirea capacităților de clasificare a imaginilor medicale într-un context de asistență medicală .  \nCuvinte cheie: sănătate, modele de învățare automată web-based, suport decizional, modul de clasificare, analiză de imagini medicale.  \n1. Introduction  \nIn the dynamic landscape of healthcare, the integration of advanced technologies is essential for enhancing diagnostic capabilities, optimizing workflows, and ultimately enhancing patient outcomes. One notable enhancement to medical information systems is the inclusion of an automated classification module powered by machine learning, representing a pivotal advancement in leveraging artificial intelligence (AI) for the analysis and interpretation of medical images (Crăciun, 2023) .  \nThe advantages of integrating such an automated classification module into a medical information system are multifaceted. Firstly, it provides a robust solution for managing the increasing volume and complexity of medical imaging data. With healthcare institutions facing a surge in diverse imaging modalities and datasets, automating image classification tasks becomes crucial in relieving the workload on healthcare professionals, allowing them to prioritize patient care (Todor et al., 2015) .  \nFurthermore, the precision and efficiency offered by machine learning algorithms significantly enhance diagnostic accuracy. Automated classification systems can swiftly and accurately categorize medical images, assisting clinicians in identifying patterns, anomalies and potential disease indicators. This expedites the diagnostic process and enhances the overall reliability of medical interpretations (Singh et al., 2022) .  \nAdditionally, the integration of such a module establishes a foundation for standardized and consistent analysis across healthcare settings. By minimizing the subjectivity associated with manual image interpretation, automated classification ensures a more uniform and reproducible diagnostic approach, promoting better collaboration among healthcare professionals and facilitating  \nseamless exchange of medical information. Moreover, as healthcare systems transition towards a patient-centric model, the incorporati","cbCaiupXfQiqzSTy","https://ap.wps.com/l/cbCaiupXfQiqzSTy","pdf",753239,16,"English","# 1. Introduction\n# 2. Materials and methods\n## 2.1. Databases","[{\"question\":\"Which web-based machine learning platforms are compared in the paper?\",\"answer\":\"Google Vertex AI, Google Teachable Machine, Azure Machine Learning, and Salesforce Einstein Vision are compared as candidates for a medical-image classification module.\"},{\"question\":\"What is the main objective of the comparative evaluation?\",\"answer\":\"To assess the platforms’ suitability for integration into a medical information system, specifically as a classification module for medical images.\"},{\"question\":\"What criteria are used to compare the models?\",\"answer\":\"The evaluation considers model accuracy, ease of integration, and scalability, and also selects based on performance measures such as F1 score and adaptability across medical image databases.\"}]","Comparative analysis of web-based machine learning models | PDF",1789640893,40,{"code":4,"msg":82,"data":100},[101,105,109,113,118,123,127,130,135,138,142],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":110,"show_sort_weight":111,"slug":112},"Exam",70,"exam",{"id":114,"doc_module":4,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},5,"Comic",60,"comic",{"id":119,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":121,"slug":122},6,"Technology",50,"technology",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":98,"slug":126},7,"Healthcare","healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":128,"slug":129},30,"research-report",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":132,"show_sort_weight":133,"slug":134},9,"Religion & Spirituality",20,"religion-spirituality",{"id":133,"doc_module":4,"doc_module_name":25,"category_name":136,"show_sort_weight":133,"slug":137},"World Cup","world-cup",{"id":139,"doc_module":4,"doc_module_name":25,"category_name":140,"show_sort_weight":139,"slug":141},10,"Lifestyle","lifestyle",{"id":143,"doc_module":4,"doc_module_name":25,"category_name":144,"show_sort_weight":114,"slug":145},19,"General","general"]