[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121721-en":3,"doc-seo-121721-105":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":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},121721,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Breast Cancer Prediction using Machine Learning Models - Slides","Breast cancer is a malignant disease originating in breast cells, and early detection can significantly improve survival. This study develops machine learning approaches to estimate the probability of breast cancer for patients using models including Multilayer Perceptron, K-Nearest Neighbors, AdaBoost, Bagging, Gradient Boosting, and Random Forest. The Wisconsin diagnostic dataset (569 observations, 32 features) is cleaned, explored, trained, tested, and validated. Model quality is assessed with accuracy, specificity, sensitivity, F1 score, and precision. Random Forest, Gradient Boosting, and AdaBoost deliver the highest performance, reaching 100% accuracy.","Breast Cancer Prediction using Machine Learning  \nModels  \nOrlando Iparraguirre-Villanueva 1, Andrés Epifanía-Huerta2,  \nCarmen Torres-Ceclén3, John Ruiz-Alvarado4, Michael Cabanillas-Carbonell5  \nFacultad de Ingeniería y Negocios, Universidad Norbert Wiener, Lima, Perú1 Facultad de Ingeniería de Sistemas, Universidad Nacional de San Martin, Perú2 Facultad de Ingeniería, Universidad Católica los Ángeles de Chimbote, Perú3 Facultad de Ingeniería, Universidad Tecnológica del Perú, Lima, Perú4  \nFacultad de Ingeniería, Universidad Privada del Norte, Lima, Perú5  \nAbstract—Breast cancer is a type of cancer that develops in the cells of the breast. Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments. Detecting cancer at an early stage can save a person's life. Artificial intelligence (AI) plays a very important role in this area. Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers. This work aims to predict the probability of breast cancer in patients. Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF). The breast cancer diagnostic medical dataset from the Wisconsin repository has been used. The dataset includes 569 observations and 32 features. Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed. The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision. The training and results indicate that the six trained models can provide optimal classification and prediction results. The RF, GB, and AB models achieved 100% accuracy, outperforming the other models. Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB. Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively. Similarly, the last three models achieved an optimal yield close to 100%. Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction.  \nKeywords—Prediction; models; machine learning, cells; breast cancer  \nI. INTRODUCTION  \nBreast cancer can be classified as a type of cancer that occurs in the cells of the breast. Both men and women can get it, although women are more likely than men to suffer from it. The process of breast cancer begins with the uncontrolled  \ngrowth of cells in the lining of the breast [1] . At first, there are no symptoms of pain or cancerous growth, and has a low potential for metastatic growth and is limited to the lobe where it grows without generating any symptoms [2],[3] . Symptoms of breast cancer can include anything from a small lump in the breast to changes in the shape of the breast or changes in the color of the skin [4], to identify breast cancer early, it is important to undergo early detection tests, as there are many types of breast cancer and many of them do not cause symptoms at first. Lobular carcinoma in situ, for example, is a type of cancer that occurs in the area of abnormal milkproducing cells of the breast. Invasive lobular carcinoma, which develops in the lobules of the milk-producing mammary glands, people with this symptom experience thickening of the breast tissue, swelling of the breast, and change in skin texture. Ductal carcinoma in situ, this type of cancer usually does not cause symptoms, it is discovered through mammography and invasive ductal is the most common type of cancer accounting for approximately 80% of cases [5]–[7] . There is solid evidence that alcohol consumption, growing older, having dense breasts, family history, radiotherapy treatments, obesity and exposure to radiation increase the risk of breast cancer [2],[8] in turn, it has been shown tha","cbCaijtqWhQDGdFC","https://ap.wps.com/l/cbCaijtqWhQDGdFC","pdf",1311622,1,11,"English","en",105,"# Introduction\n## Breast cancer background and risk factors\n## Role of machine learning in prediction","[{\"question\":\"What is the goal of this work on breast cancer prediction?\",\"answer\":\"The study aims to predict the probability of breast cancer in patients using machine learning classification models.\"},{\"question\":\"Which dataset and features are used for training the models?\",\"answer\":\"It uses the Wisconsin breast cancer diagnostic medical dataset with 569 observations and 32 features.\"},{\"question\":\"How are the models evaluated in the study?\",\"answer\":\"Performance is measured using classification accuracy, specificity, sensitivity, F1 score, and precision.\"}]","Breast Cancer Prediction using Machine Learning Models - Slides | PDF",1785806474,28,{"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-prediction-using-machine-learning-models-slides","",{"@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-prediction-using-machine-learning-models-slides/121721/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of this work on breast cancer prediction?","Question",{"text":75,"@type":76},"The study aims to predict the probability of breast cancer in patients using machine learning classification models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and features are used for training the models?",{"text":80,"@type":76},"It uses the Wisconsin breast cancer diagnostic medical dataset with 569 observations and 32 features.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated in the study?",{"text":84,"@type":76},"Performance is measured using classification accuracy, specificity, sensitivity, F1 score, and precision.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]