[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121274-en":3,"doc-seo-121274-105":30,"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":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},121274,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Assessing the Effectiveness of Machine Learning Algorithms in Breast Cancer Classification - Research Findings and Evaluation","Machine learning algorithms offer strong potential for breast cancer risk assessment and detection by extracting hidden patterns from large datasets. This research compares four algorithms—SVM, Decision Tree (C4.5), Naive Bayes (NB), and k-NN—on the original Wisconsin breast cancer dataset. Performance is evaluated using classification accuracy, precision, sensitivity, and specificity. Results show SVM achieves the highest accuracy (97.13%) with the lowest error rate. Experiments are conducted using the WEKA data mining tool in a simulated environment.","Assessing the Effectiveness of Machine Learning Algorithms in Breast Cancer Classification  \nNisharani Bhoi 1  \n1 Research Scholar, Department of Computer Application, Dr. A. P. J. Abdul Kalam University, Indore, Madhya Pradesh  \nDr. Sandeep Singh Rajpoot 2  \n2 Supervisor, Department of Computer Application, Dr. A. P. J. Abdul Kalam University, Indore, Madhya Pradesh  \nAbstract  \nBy using massive datasets and sophisticated computational methods to discover patterns and correlations that may not be visible to human eyes, machine learning algorithms have tremendous promise for enhancing breast cancer risk assessment and detection. This research compares the performance of four machine learning algorithms on the original datasets for Wisconsin breast cancer: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB), and k-Nearest Neighbors (k-NN) . The primary goal is to evaluate the efficacy of each algorithm in terms of data classification accuracy, precision, sensitivity, and specificity. In terms of accuracy (97.13%) and error rate (lowest), experimental findings demonstrate that SVM delivers the best performance. The trials are carried out using the WEKA data mining tool in a simulated setting.  \nKeywords: Machine learning, Data mining, Breast cancer, Effectiveness, Accuracy  \nI.INTRODUCTION  \nBreast cancer continues to be a very frequent and lethal illness that impacts women on a global scale. Timely identification and precise assessment of potential risks are essential for enhancing patient outcomes and minimizing fatality rates. Machine learning algorithms have become more valuable for predicting and diagnosing breast cancer. They have the potential to improve the accuracy and efficiency of screening programs and clinical decisionmaking. Machine learning algorithms may use extensive datasets of patient demographics, clinical factors, and imaging results to spot patterns and associations that may not be easily discernible to human observers. This enables more accurate risk assessment and early identification of breast cancer.  \nMachine learning algorithms use computational methods to autonomously acquire knowledge from data and generate predictions or judgments without the need for explicit programming. When it comes to predicting and diagnosing the risk of breast cancer, these algorithms may be trained using a wide range of datasets that include different sorts of information such as demographic characteristics, family  \nhistory, genetic markers, mammographic pictures, and histological results. Machine learning methods may use key characteristics and trends from these datasets to create prediction models that accurately categorize patients into distinct risk groups or identify breast tumors as either benign or cancerous.  \nA key benefit of machine learning algorithms in breast cancer risk prediction and diagnosis is their capacity to incorporate diverse data sources and discern intricate connections among factors. Conventional risk assessment models often depend on a restricted range of variables, such as age, family history, and hormonal state, which may not comprehensively include the diversity of risk factors for breast cancer. On the other hand, machine learning methods may encompass a diverse set of factors and consider how they interact with each other, resulting in more thorough and customized risk prediction models.  \nFurthermore, machine learning algorithms possess the ability to adjust and enhance their performance when they encounter fresh data, rendering them flexible and multifunctional instruments for evaluating the risk of breast cancer. Machine learning systems may adapt to shifting trends and improve their predictions by regularly updating their predictive models using input from clinical outcomesand new research results. This allows them to better represent the developing landscape of breast cancer risk factors and diagnostic criteria.  \nRecent research have shown that machine learning algorit","cbCaigHlUfo0k7gI","https://ap.wps.com/l/cbCaigHlUfo0k7gI","pdf",131086,1,4,"English","en",105,"# Introduction\n## Role of Machine Learning in Breast Cancer Prediction\n## Benefits and Adaptability\n## Challenges in Real-World Adoption\n# Review of Literature","[{\"question\":\"Which machine learning algorithms are compared for breast cancer classification?\",\"answer\":\"The study compares Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB), and k-Nearest Neighbors (k-NN).\"},{\"question\":\"How is algorithm effectiveness evaluated in the research?\",\"answer\":\"Effectiveness is assessed using classification accuracy, precision, sensitivity, and specificity, along with error rate comparison.\"},{\"question\":\"What is the best-performing algorithm and its reported accuracy?\",\"answer\":\"SVM delivers the best performance, achieving 97.13% accuracy and the lowest error rate among the tested algorithms.\"}]","Assessing the Effectiveness of Machine Learning Algorithms in Breast Cancer Classification - Research Findings and Evaluation | PDF",1785734844,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"assessing-the-effectiveness-of-machine-learning-algorithms-in-breast-cancer-classification-research-findings-and-evaluation","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/assessing-the-effectiveness-of-machine-learning-algorithms-in-breast-cancer-classification-research-findings-and-evaluation/121274/",{"url":52,"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":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which machine learning algorithms are compared for breast cancer classification?","Question",{"text":74,"@type":75},"The study compares Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB), and k-Nearest Neighbors (k-NN).","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is algorithm effectiveness evaluated in the research?",{"text":79,"@type":75},"Effectiveness is assessed using classification accuracy, precision, sensitivity, and specificity, along with error rate comparison.",{"name":81,"@type":72,"acceptedAnswer":82},"What is the best-performing algorithm and its reported accuracy?",{"text":83,"@type":75},"SVM delivers the best performance, achieving 97.13% accuracy and the lowest error rate among the tested algorithms.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]