[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122986-en":3,"doc-seo-122986-105":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},122986,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","Breast Cancer Classification Procedure Using Machine Learning Techniques - Study Overview","Breast cancer is a malignant tumor that requires early detection to enable accurate treatment and effective management. An appropriate, fast, and effective staging detection approach is therefore essential. This study classifies breast cancer stages using multiple machine learning methods, addressing imbalanced patient counts with SMOTE oversampling, selecting parameters via 10-fold cross validation, and comparing Neural Network versus k-Nearest Neighbor on oversampled training and test data, reporting higher AUC for the Neural Network (82.3%) than k-NN (80.8%).","Breast Cancer Classification Procedure Using Machine Learning Techniques  \nJerry Dwi Trijoyo Purnomo1* and Dea Restika Augustina Pratiwi1  \n1Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia  \nAbstract. Breast cancer is a malignant tumor that attacks breast tissue. This disease can be treated and managed properly if diagnosed at an early stage.  \nAn appropriate, fast and effective cancer stage detection algorithm is required so that patients can be treated precisely. In this study, the classification of breast cancer stages will be carried out using several machine learning methods. The number of patients in each stage is unequal or unbalanced as well. Therefore, the oversampling method with SMOTE is applied. The selection of the best parameters is done using 10-fold cross validation on the training data. Next, modeling was carried out using the Neural Network method, and K-Nearest Neighbor on training and testing data which had been oversampled with SMOTE. It was found that the neural network had a higher AUC value than k-Nearest Neighbor, namely 82.3%  \nwhile k-NN was 80. 8% .  \n1 Introduction  \nAs women age, the risk of breast cancer increases. However, breast cancer in young women tends to be more aggressive and has a higher stage than in older women [1]. A family history of breast cancer is an important risk factor. Women with a family history of breast cancer are significantly more likely to be diagnosed with stage III breast cancer than women without a family history of breast cancer [2] . Research regarding the identification of breast cancer characteristics and epidemiological risk factors based on age and menopause status showed that breast cancer cases for women with premenopausal status were significantly more likely to have stages II, III, or IV compared to women with postmenopausal status. Women who have a history of breast disease, especially those who have had breast cancer before, have a higher risk of suffering from breast cancer a second time. However, second breast cancer detected in the asymptomatic phase (without clinical symptoms) has a better stage than second breast cancer in the symptomatic phase, because it has a smaller tumor size and fewer metastases [3] .  \nExamination to determine the subtype of breast cancer is carried out using immunohistochemistry (IHK), namely to see the presence of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2) . CPI examination plays a role in helping determine predictions of systemic therapy response and prognosis [4]. Dunnwald, Rossing, and Li [5] conducted research with the results that women  \n* [Corresponding author : j](Corresponding author : jerrypurnomo@gmail.com)[errypurnomo@gmail.com](Corresponding author : jerrypurnomo@gmail.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nwith ER- / PR+ and ER- / PR-receptor status had larger tumors and were diagnosed with advanced breast cancer when compared with women whose receptor status was ER+ / PR+ and ER+ / PR- . Meanwhile, in research conducted by Seshadri, et al. [6] on 1056 patients with stage I-III breast cancer showed that HER2 receptor status had a significant relationship with breast cancer stage.  \nMachine learning, which is currently known as an alternative to modern classification methods that has good classification results, has also been applied in several studies with classification accuracy results that are generally better than logistic regression models. Research on the classification of breast cancer malignancies was carried out on Wisconsin City breast cancer patients by Kurniawan & Ivandri [7] by comparing the k-NN and Decision Tree methods, obtaining higher accuracy for the k-NN method, namely 94.71% . This re","cbCaibnwRkMn6HeO","https://ap.wps.com/l/cbCaibnwRkMn6HeO","pdf",507870,1,12,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Synthetic Minority Oversampling Technique (SMOTE)","[{\"question\":\"Why is breast cancer staging important in this study?\",\"answer\":\"Early and accurate staging supports precise patient treatment decisions. The paper targets a fast, effective stage detection algorithm to improve diagnostic usefulness.\"},{\"question\":\"How does the study address imbalanced patient stage data?\",\"answer\":\"The study applies oversampling using SMOTE to balance unequal patient counts across stages by generating synthetic samples.\"},{\"question\":\"Which machine learning model achieved better performance?\",\"answer\":\"The Neural Network model produced a higher AUC value (82.3%) than the k-Nearest Neighbor model (80.8%) on the oversampled data.\"}]","Breast Cancer Classification Procedure Using Machine Learning Techniques - Study Overview | PDF",1785814035,30,{"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},"breast-cancer-classification-procedure-using-machine-learning-techniques-study-overview","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/breast-cancer-classification-procedure-using-machine-learning-techniques-study-overview/122986/",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","2026-08-04",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},"Why is breast cancer staging important in this study?","Question",{"text":75,"@type":76},"Early and accurate staging supports precise patient treatment decisions. The paper targets a fast, effective stage detection algorithm to improve diagnostic usefulness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study address imbalanced patient stage data?",{"text":80,"@type":76},"The study applies oversampling using SMOTE to balance unequal patient counts across stages by generating synthetic samples.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model achieved better performance?",{"text":84,"@type":76},"The Neural Network model produced a higher AUC value (82.3%) than the k-Nearest Neighbor model (80.8%) on the oversampled data.","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},[93,97,101,105,110,115,118,122,127,130,134],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","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"]