[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117028-en":3,"doc-seo-117028-105":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},117028,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Cancer - Investigating the Impact of the Implementation Platform on Machine Learning Models - Abstract and Introduction","The study addresses whether selecting a development implementation platform affects machine learning model performance in cancer diagnosis. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from UCI, four ML algorithms are trained on two default-configured platforms: Python SciKit-Learn and KNIME Analytics. Performance metrics are evaluated and compared to determine differences attributable to platform choice. Results highlight that implementation platforms can materially influence predictive efficacy, with downstream implications for model reliability and patient outcomes. Source code and study data are publicly released via GitHub.","AIHealth 2024 : The First International Conference on AI-Health  \nCancer: Investigating the Impact of the Implementation Platform on Machine  \nLearning Models  \nAdedayo Seun Olowolayemo, Amina Souag, Konstantinos Sirlantzis  \nSchool of Engineering, Technology and Design, Canterbury Christ Church University (CCCU)  \nCanterbury, UK  \nemail: (a.olowolayemo502, amina.souag, konstantinos.sirlantzis)@[canterbury.ac.uk](canterbury.ac.uk)  \nAbstract—In the context of global cancer prevalence and the imperative need to improve diagnostic efficiency, scientists have turned to machine learning (ML) techniques to expedite diagnosis processes. Although previous research has shown promising results in developing predictive models for faster cancer diagnosis, discrepancies in outcomes have emerged, even when employing the same dataset. This study addresses a critical question: does the choice of development platform for ML models impact their performance in cancer diagnosis? Utilizing the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) dataset from the University of California, Irvine (UCI) to train four ML algorithms on two distinct platforms: Python SciKit-Learn and Knime Analytics. The algorithms’ performance was rigorously assessed and compared, with both platforms operating under their default configurations. The findings of this study underscore an impact of platform selection on ML model performance, emphasizing the need for thoughtful consideration when choosing a platform for predictive models’ development. Such a decision bears significant implications for model efficacy and, ultimately, patient outcomes in the healthcare industry. The source code (Python and Knime) and data for this study are made fully available through a public GitHub repository.  \nKeywords-Cancer; Machine Learning; Python SciKit-Learn; Knime Analytics; Wisconsin Diagnostic Breast Cancer (WDBC).  \nI. INTRODUCTION  \nCancer is a global health menace responsible for nearly 10,000,000 deaths in year 2020 alone [1][2][3] . This disease is characterized by the uncontrolled growth of body cells which forms tumors classified as malignant-the cancerous cells that are invasive and capable of spreading to other parts of the body -or benign-the non-cancerous cells that are not capable of invading nearby tissues and are less harmful. This disease's complexity spans multiple organs like the breast, kidneys, brain, lungs, prostate, ovaries, and skin, posing substantial challenges for healthcare professionals and patients alike. Despite significant progress in cancer understanding and treatment development, timely diagnosis remains critical as delays exacerbate patients'conditions, often leading to irreparable outcomes and increased mortality rates.  \nScientists are channeling substantial resources into accelerating the diagnostic process, and artificial intelligence, which has proven effective in various industries, is offering hope for quicker and more effective cancer diagnosis methods. Machine learning, a subset of artificial intelligence, has profoundly reshaped medical research, enhancing diagnostic precision, prognostic accuracy, and treatment strategies. By harnessing advanced computational techniques, ML algorithms ranging from Logistic Regression (LR) to Decision Trees (DT), Random Forests (RF), Gradient Boosting (GB) among several others for cancer diagnosis, extract insights from intricate medical data used in revolutionizing clinical decision-making and improving patient outcomes from pinpointing diseases through image analysis [4] to forecasting patient responses to therapies [5] .  \nThese ML algorithms have showcased remarkable potential in the field. However, a critical aspect that we found to be underexplored is the impact of implementation platforms on which the algorithms are trained, and models are developed, such as Python Scikit-learn and Knime analytics, on the performance of these algorithms. Therefore, understanding the nuanced influence of","cbCaikkwo4AyuQju","https://ap.wps.com/l/cbCaikkwo4AyuQju","pdf",747209,1,9,"English","en",105,"# Introduction\n## Research questions\n# Related work\n## Supervised machine learning in cancer","[{\"question\":\"What is the main research question of this study?\",\"answer\":\"The study asks whether the choice of implementation platform for machine learning models impacts their performance in cancer data classification.\"},{\"question\":\"Which dataset and platforms are used for training and evaluation?\",\"answer\":\"Models are trained on the publicly available UCI Wisconsin Diagnostic Breast Cancer (WDBC) dataset, using two platforms: Python SciKit-Learn and KNIME Analytics.\"},{\"question\":\"Which performance aspects are compared between the platforms?\",\"answer\":\"The study evaluates and compares accuracy, precision, recall, and F1-Score for the selected algorithms, assessing differences driven by platform selection.\"}]",1785673151,23,{"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},"cancer-investigating-the-impact-of-the-implementation-platform-on-machine-learning-models-abstract-and-introduction","",{"@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/cancer-investigating-the-impact-of-the-implementation-platform-on-machine-learning-models-abstract-and-introduction/117028/",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},"What is the main research question of this study?","Question",{"text":74,"@type":75},"The study asks whether the choice of implementation platform for machine learning models impacts their performance in cancer data classification.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which dataset and platforms are used for training and evaluation?",{"text":79,"@type":75},"Models are trained on the publicly available UCI Wisconsin Diagnostic Breast Cancer (WDBC) dataset, using two platforms: Python SciKit-Learn and KNIME Analytics.",{"name":81,"@type":72,"acceptedAnswer":82},"Which performance aspects are compared between the platforms?",{"text":83,"@type":75},"The study evaluates and compares accuracy, precision, recall, and F1-Score for the selected algorithms, assessing differences driven by platform selection.","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},[92,96,100,104,109,114,119,122,126,129,133],{"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":21,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]