[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290838-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290838-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","predicting-breast-cancer-intelligently-with-machine-learning-techniques-paper","Predicting Breast Cancer Intelligently with Machine Learning Techniques - Paper","","Breast cancer remains one of the leading causes of mortality among women worldwide, making early and accurate detection essential for improving survival rates. The study proposes an intelligent prediction framework that integrates clinical, imaging, and diagnostic datasets to distinguish benign and malignant tumors. It applies preprocessing steps such as normalization and missing-value handling, then performs feature selection to retain the most predictive attributes. Multiple supervised algorithms are trained, tuned, and cross-validated, using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Results show that ensemble learning, especially Random Forest, achieves the best performance and supports clinical decision-making.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/predicting-breast-cancer-intelligently-with-machine-learning-techniques-paper/290838/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/predicting-breast-cancer-intelligently-with-machine-learning-techniques-paper/290838.png","ImageObject",300,407,{"name":42,"@type":43},"Skyler","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What data sources are integrated for breast cancer prediction in this study?","Question",{"text":63,"@type":64},"The proposed system integrates clinical, imaging, and diagnostic datasets to identify patterns linked to benign and malignant tumors.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which machine learning models are implemented and compared?",{"text":68,"@type":64},"Support Vector Machine (SVM), Random Forest, Naïve Bayes, Logistic Regression, and K-Nearest Neighbors (KNN) are implemented and compared.",{"name":70,"@type":61,"acceptedAnswer":71},"How is model quality improved and how is performance evaluated?",{"text":72,"@type":64},"Data preprocessing (normalization and missing value handling), feature selection, hyperparameter tuning, and cross-validation are used. Models are evaluated with accuracy, precision, recall, F1-score, and ROC-AUC/AUC.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290838,1789642723,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":55,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":80,"read_time":143,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Predicting Breast Cancer Intelligently with Machine Learning Techniques  \nManimozhi I.1, Laksmi D.2*  \n1Department ofCSE, AMET Deemed to be University, Kanathur-603112, India 2Department of EEE, AMET Deemed to be University, Kanathur-6003112, India  \n* Email: [lakshmiee@gmail.com](lakshmiee@gmail.com)  \nAbstract  \nBreast cancer remains one of the leading causes of mortality among women worldwide, necessitating early and accurate detection to improve survival rates. This study presents an intelligent breast cancer prediction framework using advanced machine learning techniques. The proposed system integrates clinical, imaging, and diagnostic datasets to identify patterns associated with benign and malignant tumours. Data preprocessing techniques, including normalization and missing value handling, are applied to ensure data quality. Feature selection methods are employed to extract the most relevant attributes influencing prediction performance. Multiple machine learning algorithms, such as Support Vector Machine (SVM), Random Forest, Naïve Bayes, Logistic Regression, and K-Nearest Neighbors (KNN), are implemented and compared. The models are evaluated using performance metrics including accuracy, precision, recall, F1-score, and Area under the Curve (AUC) . Hyper parameter tuning and cross-validation techniques are utilized to enhance model robustness and generalization. Experimental results indicate that ensemble learning methods, particularly Random Forest, achieve superior prediction accuracy compared to other models. The proposed approach demonstrates the potential of intelligent systems in supporting early diagnosis and clinical decision-making. This research contributes to the development of reliable and efficient breast cancer prediction systems, ultimately aiding healthcare professionals in improving patient outcomes.  \nKeywords  \nBreast Cancer Detection, Intelligent Prediction Systems, Supervised Learning, Ensemble Learning, Clinical Data Analysis, Radiological Data Fusion, Model Optimization, ROC-AUC, Precision Medicine, Decision Support Systems  \nIntroduction  \nOne of the leading causes of cancer related deaths worldwide is breast cancer. By diagnosing cancer in the early stage significantly we can increase the chances of survival by providing the  \nSubmission: 1 0 March 2 0 2 6 ; Acceptance: 13 April 2026; Available online: April 2026  \nCopyright: © 2026 . All the authors listed in this paper. The distribution, reproduction, and any other usage of the content of this paper is permitted, with credit given to all the author(s) and copyright owner(s) in accordance to common academic practice. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, as stated in the website: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/by/4.0/)[by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \ntreatment in that early stage, but this process involves various advantages and disadvantage to find breast cancer symptoms in the early stages. Systems that use computer diagnosis showed promise in raising diagnostic prediction and improving accuracy. But however, the likelihood of death can be considerably decreased by early prediction and prevention. It is critical to find breast cancer as soon as feasible.  \nBreast cancer prediction using machine learning involves developing algorithms that analyze data from breast cancer patients to identify patterns and make predictions about the likelihood of an individual having breast cancer. ML models can be trained on various features such as age, family history, genetic markers, and  \nMedical test results classify patients into different risk categories (e.g., benign or malignant tumors) . In this research we are accessing the various machine learning models to ensure the accuracy of the result. The ML models are trained by the dataset of the patient and in the final stage it provides t","cbCaihWWkA2rUth1","https://ap.wps.com/l/cbCaihWWkA2rUth1","pdf",151230,"English","# Abstract\n# Introduction\n# Literature Review","[{\"question\":\"What data sources are integrated for breast cancer prediction in this study?\",\"answer\":\"The proposed system integrates clinical, imaging, and diagnostic datasets to identify patterns linked to benign and malignant tumors.\"},{\"question\":\"Which machine learning models are implemented and compared?\",\"answer\":\"Support Vector Machine (SVM), Random Forest, Naïve Bayes, Logistic Regression, and K-Nearest Neighbors (KNN) are implemented and compared.\"},{\"question\":\"How is model quality improved and how is performance evaluated?\",\"answer\":\"Data preprocessing (normalization and missing value handling), feature selection, hyperparameter tuning, and cross-validation are used. Models are evaluated with accuracy, precision, recall, F1-score, and ROC-AUC/AUC.\"}]","Predicting Breast Cancer Intelligently with Machine Learning Techniques - Paper | PDF",23]