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The work evaluates machine learning algorithms—including SVM, decision tree, random forest, logistic regression, KNN, and ANN—using the CDP Breast Cancer dataset and assesses accuracy, precision, recall, sensitivity, and specificity. It also integrates CDP nano biosensor electrochemical data with immunohistochemical protein expression information, then applies supervised learning to analyze recurrence risk and improve diagnostic accuracy for clinical decision-making.",{"@graph":69,"@context":115},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/enhancing-breast-cancer-diagnosis-through-machine-learning-algorithms/369635/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/enhancing-breast-cancer-diagnosis-through-machine-learning-algorithms/369635.png","ImageObject",300,407,{"name":92,"@type":93},"Rizky","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109],{"name":110,"@type":111,"acceptedAnswer":112},"How are CDP nano biosensors and immunohistochemistry combined?","Question",{"text":113,"@type":114},"CDP nano sensors provide electrochemical identification signals with high sensitivity and selectivity, while immunohistochemistry supplies histopathological information to quantify protein expression patterns. Together, they support molecular-level biomarker analysis for model training and risk assessment.","Answer","https://schema.org",{"og:url":83,"og:type":117,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":119,"canonical":83},"index,follow",{"doc_id":121,"site_id":62},369635,1790437380,{"code":4,"msg":5,"data":124},{"doc_id":121,"user_id":125,"nickname":92,"user_avatar":126,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":127,"file_id":128,"file_url":129,"file_type":130,"file_size":131,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":132,"language":133,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":134,"faqs":135,"seo_title":136,"seo_description":67,"update_tm":137,"read_time":138},962085564807,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nEnhancing breast cancer diagnosis through machine learning algorithms  \nJavadAmraei1, Aboulfazl Mirzapoor1,2,3􀀍, Kiomars Motarjem4 & Mohammad Abdolahad5  \nAmong the most important health concerns in the world, and the number one cause of death in women, is breast cancer. Bearing in mind that there are more than 100 types of cancer, each presenting different symptoms, its early detection is indeed a big challenge. The prevalence of breast cancer indicates the prudent need for effective diagnostic and prognostic approaches. From 2016 to 2020, 19.6 deaths per 100, 000 women occurred annually due to breast cancer—a factor indicating the importance of early treatments. Machine Learning has become important for the improvement of early diagnosis and prognosis that may have the potential to reduce mortality rates. Some considerable volume of research work has been carried out on the use of Machine Learning algorithms for accurate diagnosis of breast cancer. In this research has done extensive algorithm evaluation like SVM, DT, RF, Logistic regression, KNN and ANN using datasets CDP Breast Cancer. The important performance indicators observed for each of these algorithms included accuracy, precision, recall, sensitivity, and specificity. These findings are an important step forward in the application of Machine Learning to improve diagnostic accuracy, thus enabling early detection and mitigating the major consequences of breast cancer on global health.  \nKeywords Machine learning, Breast cancer, CDP Nano Biosensor, Early detection  \nAbbreviations  \nSVM Support vector machine RF Random Forest  \nANN Artificial neural network DT Decision Tree  \nKNN K-nearest neighbors CDP Cancer diagnostic probe  \nBreast cancer is known as a significant global health concern, Millions of women are affected by it every year1,2. Reducing mortality and increasing treatment efficacy depend on prompt diagnosis and precise risk assessment. The identification of breast cancer biomarkers by innovative techniques with high sensitivity, accuracy, and quicker treatment potential has been made possible by recent developments in medical technology and nanotechnology, notably in biosensors and the use of nanoparticles for therapy. On the other hand, the emergence of machine learning (ML) methods is also a strong and powerful tool for analyzing and extracting biological data. All these methods help in the early detection of cancer cells3–6.  \nThe integration of CDP biosensors and immunohistochemically data provides a new approach to the analysis of breast cancer biomarkers at the molecular level. The CDP Nano sensor whose mechanism of action is based on electrochemical reactions with high sensitivity and selectivity allows the identification of biomolecules, this device detects the existence or absence of cancer cells by measuring the amount of RAS in the cancer environment. Also, immunohistochemistry provides valuable histopathological information that enables the quantification of protein expression patterns in breast tissue samples7–9.  \nIn this paper, we examine the risk of breast cancer recurrence by combining CDP nano sensor data and immunohistochemistry with machine learning methods that include supervised learning. We discuss the  \n1Department of Nanobiotechnology, Faculty of Biological Sciences, Tarbiat Modares University, P.O. Box 14115-  \n175, Tehran, Iran. 2Advanced and Smart Nanobiosystems Lab, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran. 3Wearable Nanosensors Lab, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran. 4Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, P.O. Box 14117-13116, Tehran, Iran. 5Nano Bio Electronic Devices Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. 􀀍 email: [a.mirzapour@modares.ac.ir](a.mirzapour@mo","cbCaijYxeD7uZHnC","https://ap.wps.com/l/cbCaijYxeD7uZHnC","pdf",2563000,18,"English","# Literature review\n## Breast cancer diagnosis using machine learning models\n## Dataset and algorithm evaluation overview","[{\"question\":\"How are CDP nano biosensors and immunohistochemistry combined?\",\"answer\":\"CDP nano sensors provide electrochemical identification signals with high sensitivity and selectivity, while immunohistochemistry supplies histopathological information to quantify protein expression patterns. Together, they support molecular-level biomarker analysis for model training and risk assessment.\"}]","Enhancing breast cancer diagnosis through machine learning algorithms | PDF",1790183773,45]