[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119286-en":3,"doc-seo-119286-105":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":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":27,"seo_description":14,"update_tm":28,"read_time":29},119286,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Analysis and Visualization of Breast Cancer Prediction through Machine Learning Models","This research investigates breast cancer prediction using multiple machine learning classifiers, emphasizing Logistic Regression, K-Nearest Neighbors, Support Vector Classifier, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and XGBoost. A Kaggle-derived dataset of clinical features is used to train and evaluate models through precision, recall, F1-score, and accuracy. The work further applies visualization and statistical analysis to interpret results, highlighting which algorithms deliver stronger predictive performance. Findings support improved diagnostic decision-making and classification in medical diagnostics.","Analysis and Visualization of Breast Cancer Prediction through Machine Learning Models  \n1Ayepeku .O. Felix*, 2Omosola .J. Olabode, 3Ayeni .J. Kehinde  \n1-2Dept. of Mathematical and Computing Science, Thomas Adewumi University Oko-Irese 3Dept. Of Computer Science, Kwara State Polytechnic, Ilorin  \nemail: [olukayode.ayepeku@tau.edu.ng](olukayode.ayepeku@tau.edu.ng)  \n(received: 17 April 2024, revised: 22 April 2024, accepted: 22 April 2024)  \nAbstract  \nThis research presents an in-depth exploration of breast cancer prediction through the application of machine learning models, specifically focusing on Logistic Regression, K-Nearest Neighbors, Support Vector Classifier, 'Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, AdaBoost Classifier, and XGBoost Classifier. The study utilizes a comprehensive dataset comprising clinical features extracted from Kaggle. Various algorithms are employed, and a meticulous analysis of precision, recall, F1-score, and accuracy is conducted to assess model performance. Through advanced visualization techniques and statistical analysis, the research provides insights into the effectiveness of machine learning models in predicting breast cancer. The outcomes ofthis study aim to contribute valuable knowledge to the field of medical diagnostics, emphasizing the importance of machine learning methodologies in enhancing breast cancer prediction and classification.  \nKeywords: Breast Cancer, Efficiency, Performance measurement, Analysis, Visualization  \n1. Introduction  \nA malignant tumor discovered in breast tissue provides the basis for the diagnosis of breast cancer. A malignant tumor is a specific kind of tumor that can spread to neighboring cells or potentially across the body. Men and women can both get breast cancer, although women are more likely to have it. [reddy 2022) . In 2020, 685 000 people worldwide died from breast cancer, accounting for 2.3 million new diagnoses. Breast cancer is the most common cancer worldwide, with 7.8 million women alive as of the end of 2020 who had received a diagnosis during the previous five years. All across the world, breast cancer affects women at any age after adolescence; however, its prevalence rises with age. From the 1930s through the 1970s, when surgery alone was the main form of therapy, there was minimal improvement in the death rate from breast cancer (radical mastectomy) . Survival rates started to rise in the 1990s as nations implemented early detection systems for breast cancer that were connected to all-encompassing treatment regimens that included efficient (Rautalin, Jahkola, and Roine 2022) of the various oncology case categories; 11.6% involved breast cancer, with women accounting for 24.2% of those cases. Any new hard mass or lump in the breast tissue is an indication of breast cancer. But not every protrusion is malignant. Cancerous masses can be seen using mammography. Only 78% of women with cancer receive a correct diagnosis from a mammogram. (Garg and Gupta, 2020) . Breast cancer is still a major public health issue, which is driving the demand for sophisticated diagnostic methods and therapeutic approaches. The nexus of technology and healthcare has made novel ways possible in response to this problem, such as the use of machine learning algorithms in the study of breast cancer. These algorithms show promise in improving tailored medicines, forecasting treatment results, and improving diagnostic accuracy through the use of computational approaches. This use of machine learning in breast cancer research represents a viable direction for enhanced patient care, providing an analytical viewpoint to support conventional approaches. In light of this, investigating the function of machine learning algorithms in the analysis of breast cancer becomes crucial for the development of precision medicine in oncology.  \nThis study compares the results and interprets the performance evaluation of eight methods, which res","cbCaifaAMcGQt7f3","https://ap.wps.com/l/cbCaifaAMcGQt7f3","pdf",416919,1,10,"English","en",105,"# Introduction\n## Literature Review\n# Research Methodology\n## Data Analysis and Interpretation\n# Conclusion and Future Work","[{\"question\":\"Which methods achieve the highest-ranked performance according to the study?\",\"answer\":\"Random Forest and XGBoost are reported as the top-performing methods for diagnosing breast cancer patients compared with the other six approaches.\"}]","Analysis and Visualization of Breast Cancer Prediction through Machine Learning Models | PDF",1785723512,25,{"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},"analysis-and-visualization-of-breast-cancer-prediction-through-machine-learning-models","",{"@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/analysis-and-visualization-of-breast-cancer-prediction-through-machine-learning-models/119286/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Which methods achieve the highest-ranked performance according to the study?","Question",{"text":75,"@type":76},"Random Forest and XGBoost are reported as the top-performing methods for diagnosing breast cancer patients compared with the other six approaches.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]