[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125436-en":3,"doc-seo-125436-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125436,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Optimizing Breast Cancer Prediction by Applying Machine Learning","Breast cancer ranks among the most prevalent and fatal cancers affecting women globally, creating an urgent need for AI decision support in healthcare. This study performs a comparative evaluation of selected machine learning algorithms, focusing on SVM, XGBoost, and ANN, using multiple parameter combinations on the breast cancer dataset. Accuracy, precision, recall, and F1-score are used to assess and compare models. The best-performing approach predicts chronic breast cancer disease with SVM using eight parameters (excluding mitosis), achieving 0.96 accuracy and 0.98 sensitivity.","Optimizing Breast Cancer Prediction by Applying Machine Learning  \nVina Nurmadani 1, Indah Suciati 2, Yoga Aji Sukma3, Linda Rassiyanti4  \n1,2,3,4 Institut Teknologi Sumatera, Indonesia  \n*[corresponding author:](corresponding author: vina.nurmadani@sd.itera.ac.id)[ vina.nurmadani@sd.itera.ac.id](corresponding author: vina.nurmadani@sd.itera.ac.id)  \nReceived July 31, 2025; Received in revised August 10, 2025; Accepted August 12, 2025  \nAbstract. In 2015, breast cancer ranked among the most prevalent and fatal cancers affecting women globally. Artificial intelligence is urgently needed to help medical professionals make more accurate decisions, reduce overdiagnosis, and streamline the diagnostic process. This study will implement and perform a comparative study of selected machine learning techniques algorithms, with a focus on SVM, XGBoost, and ANN, with various parameter combinations on the breast cancer dataset. Performance metrics such as accuracy, precision, recall, and F1-score were employed to evaluate and compare the algorithms. The results of this study show that the best model for predicting chronic breast cancer disease, which can help medical professionals predict chronic disease so that it can be treated quickly and accurately, is the SVM method using  \n8 parameters without the mitosis parameter: Clump thickness, Cell Size Uniformity, Cell Shape Uniformity, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, and Normal Nuclei, with an accuracy value of 0.96 and a sensitivity value of 0.98.  \nKeywords: ANN; breast cancer; machine learning; SVM; XGBoost  \nThis is an open access article under the Creative Commons Attribution 4.0 International License  \nINTRODUCTION  \nBreast cancer was one of the most common and deadly types of cancer for women worldwide in 2015 (Sun et al., 2017). According to the World Health Organization (WHO), in 2022, there will be 2.3 million women diagnosed with breast cancer and as many as 670,000 deaths worldwide (WHO: BREAST CANCER, 2024) . In 2017, an estimated 30% of new cancer cases (252,710) in women were breast cancer (Siegel et al., 2017) . Breast cancer is a cancer that forms in breast tissue, where breast tissue cells change and divide uncontrollably, usually resulting in a mass or lump (Houssein et al., 2021) . In more severe stages, these tissue cells can spread through the lymph nodes to other organs of the body.  \nThe high number of deaths due to breast cancer requires early detection to reduce the risk of death. Artificial intelligence is beginning to be developed in the healthcare sector, playing a role in disease identification. Medical personnel urgently need AI to make more accurate decisions, reduce misdiagnosis, and streamline the diagnostic process. Machine learning can be applied to detect diseases using classification and clustering methods for decisionmaking (Mahesh, 2020) . Numerous studies have investigated the use of machine learning to detect breast cancer using Decision Tree, Random Forest, Logistic Regression, Naive Bayes, K-nearest Neighbor, and Support Vector Machine methods, achieving the best  \naccuracy of 96.82% with the Random Forest method (El_Rahman, 2021) . Another breast cancer study using the Naive Bayes method achieved 80% accuracy with 116 datasets (Mubarog et al., 2021) . This study will apply and compare several machine learning algorithms, such as ANN, SVM, and XGBoost, with a combination of parameters on the breast cancer dataset. Model evaluation will be conducted through performance assessment based on accuracy, precision, recall, and F1-score to determine the best-performing algorithm. Therefore, the results of this study are expected to facilitate the progress of a fast and accurate medical decision support system for diagnosing breast cancer.  \nRESEARCH METHODS  \nThis study utilizes the Breast Cancer Dataset sourced from the UCI Machine Learning Repository (Wolberg, 1992) . This dataset is general and can be used fr","cbCaibSYKFmGVd8a","https://ap.wps.com/l/cbCaibSYKFmGVd8a","pdf",398944,1,6,"English","en",105,"# Abstract\n# Introduction\n# Research Methods\n## Dataset and Preprocessing\n## Machine Learning Classifiers\n## Evaluation Metrics","[{\"question\":\"Which machine learning algorithms are compared for breast cancer prediction?\",\"answer\":\"The study compares SVM, XGBoost, and ANN using parameter combinations on the breast cancer dataset.\"},{\"question\":\"What dataset and target labels are used in the study?\",\"answer\":\"The study uses the Breast Cancer Dataset from the UCI Machine Learning Repository, with class labels where 0 indicates benign and 1 indicates malignant.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is measured using metrics derived from a confusion matrix, including accuracy, precision, recall, and F1-score.\"}]","Optimizing Breast Cancer Prediction by Applying Machine Learning | PDF",1785898909,15,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimizing-breast-cancer-prediction-by-applying-machine-learning","",{"@graph":36,"@context":85},[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/optimizing-breast-cancer-prediction-by-applying-machine-learning/125436/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are compared for breast cancer prediction?","Question",{"text":75,"@type":76},"The study compares SVM, XGBoost, and ANN using parameter combinations on the breast cancer dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and target labels are used in the study?",{"text":80,"@type":76},"The study uses the Breast Cancer Dataset from the UCI Machine Learning Repository, with class labels where 0 indicates benign and 1 indicates malignant.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated?",{"text":84,"@type":76},"Performance is measured using metrics derived from a confusion matrix, including accuracy, precision, recall, and F1-score.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"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":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]