[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117292-en":3,"doc-seo-117292-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"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":13,"seo_description":14,"update_tm":27,"read_time":28},117292,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Evaluating the impact of machine learning platforms on cancer classification model performance - A cross-platform comparative study","Machine Learning techniques advance predictive models for early cancer detection, yet implementation platforms’ influence on model performance remains insufficiently examined, especially when the same dataset yields different results. This study compares three ML tools—Scikit-learn, KNIME, and MATLAB—on four classifiers: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting. Using the Wisconsin Diagnostic Breast Cancer dataset under default settings, performance is assessed by accuracy, recall, precision, and F1-score.","Research Space  \nJournal article  \nEvaluating the impact of machine learning platforms on cancer classification model performance: A cross-platform comparative study  \nOlowolayemo, A., Souag, A. and Sirlantzis, K.  \nThis is the accepted version of the paper published  \nat [https://www.iariajournals.org/l](https://www.iariajournals.org/l)ife sciences/lifsci v16 n34 2024 page...  \nEvaluating the Impact of Machine Learning Platforms on Cancer Classification Model Performance: A Cross-Platform Comparative Study  \nAdedayo Seun Olowolayemo  \nSchool of Engineering, Technology, and Design Canterbury Christ Church University (CCCU) Canterbury, UK  \n[a.olowolayemo502@canterbury.ac.uk](a.olowolayemo502@canterbury.ac.uk)  \nAmina Souag  \nSchool of Engineering, Technology, and Design Canterbury Christ Church University (CCCU) Canterbury, UK  \n[amina.souag@canterbury.ac.uk](amina.souag@canterbury.ac.uk)  \nKonstantinos Sirlantzis  \nSchool of Engineering, Technology, and Design Canterbury Christ Church University (CCCU) Canterbury, UK  \n[Konstantinos.sirlantzis@canterbury.ac.uk](Konstantinos.sirlantzis@canterbury.ac.uk)  \nAbstract—Machine Learning techniques have become pivotal in advancing predictive models for early cancer detection, addressing the growing need for improved diagnostic efficiency. However, the role of implementation platforms in influencing model performance remains underexplored, even as variations in performance with the same dataset raise questions about platform choice. This study evaluates the impact of three ML implementation tools, the Scikit-learn, KNIME, and MATLAB on the performance of four classification algorithms: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting. Using the publicly available Wisconsin Diagnostic Breast Cancer dataset, these algorithms were implemented under default configurations and compared across key metrics: accuracy, recall, precision, and F1-score. Results revealed significant platform-dependent variations: Scikit-learn achieved consistently higher recall, particularly for Random Forest and Gradient Boosting, making it more effective at minimising false negatives critical in cancer diagnosis. MATLAB demonstrated superior precision, especially for Random Forest and Gradient Boosting, indicating potential in reducing false positives. KNIME, while effective in specific contexts, underperformed in recall and precision, raising concerns in scenarios requiring high sensitivity and specificity. These findings underscore the importance of platform selection based on predictive task requirements, especially in healthcare, where balancing false positives and false negatives is crucial. The study provides actionable insights for selecting ML platforms to enhance diagnostic accuracy in cancer classification tasks, with source code and data fully accessible through a public GitHub repository.  \nKeywords-Cancer; Machine Learning; Python Scikit-learn; KNIME; MATLAB; Wisconsin Diagnostic Breast Cancer.  \nI. INTRODUCTION  \nCancer remains a significant global health threat, causing nearly 10 million deaths in 2020 approximately one in six deaths globally underscoring its devastating impact and the urgent need for more effective prevention, early detection,  \nand treatment strategies [1][2][3][4] . According to the World Health Organization (WHO), the disease affects individuals of all ages, including about 400,000 children each year. Notably, breast, lung, and colorectal cancers had the highest incidence rates, with lung cancer leading in mortality, followed by colorectal, liver, stomach, and breast cancers, as shown in Fig. 1. This figure depicts the distribution of new cancer cases and cancer-related deaths by type for 2020, highlighting the global burden of specific cancers and emphasizing the importance of early diagnosis and screening to reduce mortality and mitigate the far-reaching impacts of the disease [5] .  \nCancer arises from the uncontrolled division of cells, resulting i","cbCaijeiUY5fuOd2","https://ap.wps.com/l/cbCaijeiUY5fuOd2","pdf",1086792,1,17,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Which machine learning platforms are evaluated in the study?\",\"answer\":\"The study evaluates Scikit-learn, KNIME, and MATLAB as implementation tools for cancer classification models.\"},{\"question\":\"How is model performance measured across platforms?\",\"answer\":\"Models are compared using accuracy, recall, precision, and F1-score computed from the Wisconsin Diagnostic Breast Cancer dataset.\"},{\"question\":\"What key findings relate platform choice to cancer diagnosis outcomes?\",\"answer\":\"Platform-dependent differences appear across metrics: Scikit-learn tends to improve recall, MATLAB can improve precision, and KNIME shows weaker recall and precision, which may affect sensitivity and specificity trade-offs.\"}]",1785675032,43,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"evaluating-the-impact-of-machine-learning-platforms-on-cancer-classification-model-performance-a-cross-platform-comparative-study","",{"@graph":35,"@context":84},[36,53,67],{"@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/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/evaluating-the-impact-of-machine-learning-platforms-on-cancer-classification-model-performance-a-cross-platform-comparative-study/117292/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which machine learning platforms are evaluated in the study?","Question",{"text":74,"@type":75},"The study evaluates Scikit-learn, KNIME, and MATLAB as implementation tools for cancer classification models.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is model performance measured across platforms?",{"text":79,"@type":75},"Models are compared using accuracy, recall, precision, and F1-score computed from the Wisconsin Diagnostic Breast Cancer dataset.",{"name":81,"@type":72,"acceptedAnswer":82},"What key findings relate platform choice to cancer diagnosis outcomes?",{"text":83,"@type":75},"Platform-dependent differences appear across metrics: Scikit-learn tends to improve recall, MATLAB can improve precision, and KNIME shows weaker recall and precision, which may affect sensitivity and specificity trade-offs.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]