[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118693-en":3,"doc-seo-118693-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118693,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning-Driven Classification Techniques for Early-Stage Breast Cancer","Breast cancer is one of the most prevalent cancers among women worldwide, making early detection a decisive factor for prognosis and survival. Conventional detection from mammograms and other medical imaging suffers from constraints such as limited sensitivity and processing difficulties. Machine learning models are trained to detect subtle tissue changes and image patterns before visible tumors, improving accuracy and specificity. Results indicate the need for standardized data acquisition and analysis protocols and for addressing dataset diversity issues that can introduce bias and reduce generalizability.","Machine Learning–Driven Classification Techniques for Early-Stage Breast Cancer  \n1 T. Vinothini, 2 Dr. V. Arun, 3 Dr. S. Meenakshi Sundaram  \n1 Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India.  \n2 Department of Electronics and Communication Engineering, Anna University Regional Campus, Madurai, Tamil Nadu, India.  \n3 Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India.  \n(Received: 27 September 2025 Revised: 05 october 2025 Accepted:01 November 2025)  \n\n| KEYWORDS\u003Cbr>Random Forest, Decision Tree, Machine Learning, Logistic Regression, Breast Cancer, Prediction, and Detection. | ABSTRACT:\u003Cbr>One of the most prevalent cancers in women worldwide is breast cancer. In addition to developing standardized procedures for data collecting and analysis of breast cancer using mammograms and other medical imaging data, more research is needed to overcome the difficulties and constraints of the conventional detection process. In recent years, a number of studies have been conducted to develop and evaluate machine learning models for breast cancer diagnosis, recurrence prediction, and treatment planning. Even before a tumor appears on a mammogram, as is typically the case, machine learning algorithms may be trained to identify minute alterations in breast tissue that could be signs of cancer. Higher accuracy and specificity have been achieved in the detection of breast cancer with the use of machine learning techniques, which makes it a useful tool for supporting doctors in making clinical decisions. Overall, the results suggest that further research may be necessary to develop standardized procedures for data collecting and analysis as well as to address the issues and constraints of machine learning in the detection of breast cancer. |\n| --- | --- |\n\n1. Introduction  \nDespite significant advancements in breast cancer screening and treatment, early detection remains critical for improving prognosis and survival rates. However, mammography has limitations, including its low sensitivity, it can also boost the accuracy and efficiency of existing screening methods. It allows for the identification of patterns and relationships within large amounts of data that may be difficult for human experts to identify. These limitations have alternative approaches to breast cancer screening, including the use of ML techniques.  \nMachine learning has widely used in medical imaging analysis, including breast cancer detection using mammograms and other medical images. ML models can be trained to recognize specific patterns in medical images that are indicative of breast cancer, allowing for earlier detection and more accurate diagnosis. Machine learning algorithms can also be trained to recognize subtle changes in breast tissue that may be indicative of cancer, even before a tumor is visible on a mammogram.  \nby selecting the most relevant features from the input data. The use of ML techniques has demonstrated high accuracy and specificity in breast cancer detection, making it a promising tool for aiding physicians in clinical decision-making.  \nThe use of ML techniques has demonstrated high accuracy and specificity in breast cancer detection, making it a promising tool for aiding physicians in clinical decision-making. ML models can analyze large amounts of data in a short amount of time and provide accurate predictions, reducing the need for unnecessary diagnostic tests and surgeries. Furthermore, ML models can help identify subgroups of patients with a high risk of breast cancer, enabling personalized screening and treatment plans. One major challenge is the lack of standardized protocols for data acquisition and analysis, which can lead to inconsistent results across studies. Additionally, the lack of diverse and representative datasets can limit the generalizability of ML models, leading to bias and errors.  \nThe use ","cbCaip3iAfjpO4r0","https://ap.wps.com/l/cbCaip3iAfjpO4r0","pdf",444325,1,6,"English","en",105,"# Introduction\n## Literature Review","[{\"question\":\"Why is early detection of breast cancer critical?\",\"answer\":\"Early detection improves prognosis and survival rates. The document emphasizes that despite advances in screening and treatment, early detection remains essential.\"},{\"question\":\"How do machine learning models improve breast cancer detection from mammograms?\",\"answer\":\"ML models learn image patterns and subtle tissue changes that may indicate cancer even before tumors are visible. This supports earlier detection and more accurate diagnosis with higher specificity and accuracy.\"},{\"question\":\"What challenges limit machine learning breast cancer models?\",\"answer\":\"The document highlights lack of standardized protocols for data acquisition and analysis and limited diverse, representative datasets. These issues can cause inconsistent results across studies and reduce generalizability due to bias and errors.\"}]","Machine Learning-Driven Classification Techniques for Early-Stage Breast Cancer | PDF",1785684909,15,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-driven-classification-techniques-for-early-stage-breast-cancer","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-driven-classification-techniques-for-early-stage-breast-cancer/118693/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early detection of breast cancer critical?","Question",{"text":76,"@type":77},"Early detection improves prognosis and survival rates. The document emphasizes that despite advances in screening and treatment, early detection remains essential.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do machine learning models improve breast cancer detection from mammograms?",{"text":81,"@type":77},"ML models learn image patterns and subtle tissue changes that may indicate cancer even before tumors are visible. This supports earlier detection and more accurate diagnosis with higher specificity and accuracy.",{"name":83,"@type":74,"acceptedAnswer":84},"What challenges limit machine learning breast cancer models?",{"text":85,"@type":77},"The document highlights lack of standardized protocols for data acquisition and analysis and limited diverse, representative datasets. 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