[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121954-en":3,"doc-seo-121954-105":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},121954,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",7,"Healthcare","Enhancing Tumor Classification Through Machine Learning Algorithms for Breast Cancer Diagnosis - Research","Machine learning can strengthen breast cancer diagnosis by supporting clinicians with faster, more accurate classification decisions. The study evaluates five commonly used models, including logistic regression, support vector machines, random forest, decision trees, and deep neural networks, for labeling tumors as benign or malignant using the Wisconsin breast cancer dataset. Random forest identifies 17 features via cross-validation and achieves strong validation performance, while grid search optimizes tree depth. SMOTE addresses class imbalance and improves overall separability, with the best models showing high accuracy and AUC values.","Enhancing Tumor Classification Through Machine Learning Algorithms for Breast Cancer Diagnosis  \nLawrence Agbota 1, Edmund Agyemang 1,2,3*, Priscilla Kissi-Appiah 1, Lateef Moshood 1, Akua OseiNkwantabisa 1, Vincent Agbenyeavu 1, Abraham Nsiah4, Augustina Adjei5  \n1. School of Mathematical and Statistical Science, College of Sciences, University of Texas Rio Grande Valley, USA.  \n2. University of Ghana, Department of Statistics and Actuarial Science, Ghana  \n3. Department of Computer Science, Ashesi University, No.1 University Avenue, Berekuso, Ghana.  \n4. Department of Statistics, Ball State University, Muncie-USA  \n5. Ghana Health Service, Koforidua-Ghana.  \n* E-mail of the corresponding author: [edmundfosu6@gmail.com](edmundfosu6@gmail.com)  \nAbstract  \nIn cancer diagnosis, machine learning helps improve cancer detection by providing doctors with a second perspective and allowing for faster and more accurate determination and decisions. Numerous studies have used both classic machine learning approaches and deep learning to address cancer classification. In this study, we examine the efficacy of five commonly used machine learning algorithms; both traditional and deep learning models namely, Logistic Regression, Support Vector Machines (SVM), Random Forest (RF), Decision Tree and Deep Neural Networks (DNN) . We analyze their ability to properly classify tumors as Benign or Malignant using the Wisconsin breast cancer dataset (WBCD) . Random Forest classifier was employed to reduce model complexity, successfully narrowing down the number of features to 17 through cross-validation and achieving a validation score of 96.84%. Subsequently, a grid search was used to determine the maximum tree depth, resulting in five. The Synthetic Minority Oversampling Technique (SMOTE) was employed as a resampling tool to balance the Benign and Malignant categories adequately solving the class imbalance problem encountered in classification problems. After evaluating the overall performance for the unbalanced data, Random Forest emerged as the best classification model with an accuracy of 98.20%, followed by Logistic Regression with an accuracy of 97.40% . However, after applying SMOTE, both Random Forest and Logistic Regression emerged as the best models both with an accuracy of 94.70% . Both Random Forest and Logistic Regression models had an outstanding performance with an area under the curve (AUC) value of 0.997 and 0.994 respectively. Keywords: Breast Cancer, Random Forest, Logistic Regression, Support Vector Machines, Deep Neural Networks, Synthetic Minority Oversampling Technique.  \nDOI: 10.7176/CEIS/15-1-08  \nPublication date: June 30th 2024  \n1 Introduction  \nBreast cancer remains one of the most common cancers among women worldwide, significantly impacting public health [1]. It is the leading cause of cancer-related deaths among women, affecting millions each year. Despite advances in treatment and early detection, the diagnosis of breast cancer at later stages can significantly diminish survival rates and increase treatment complexities. Early and accurate diagnosis is crucial for effective treatment and better patient outcomes. Given its high prevalence and the severe implications of delayed or incorrect diagnosis, there is a pressing need for innovative and more reliable diagnostic methods. This backdrop sets the stage for exploring enhanced machine learning techniques that can potentially transform the landscape of breast cancer diagnosis, promising more accurate, timely interventions. Breast cancer if left unchecked, the tumors can spread throughout the body and become fatal. Breast cancer cells begin inside the milk ducts and/or the milk-producing lobules of the breast. The earliest form (in situ) is not life-threatening and can be detected in early stages. Cancer cells can spread into nearby breast tissue (invasion) . This creates tumors that cause lumps or thickening [2] . Invasive cancers can spread to nearby lymph nodes or ","cbCaiaeqKOSgGwrv","https://ap.wps.com/l/cbCaiaeqKOSgGwrv","pdf",1707505,1,15,"English","en",105,"# Introduction\n## Breast cancer background and diagnosis challenges\n# Methodology\n## Dataset and class labeling\n## Machine learning models compared\n## Feature selection and hyperparameter tuning\n## Handling class imbalance with SMOTE\n# Results and Performance Evaluation\n## Accuracy and validation outcomes\n## AUC comparison across models\n# Discussion\n## Model selection and implications for clinical support","[{\"question\":\"Which machine learning algorithms are compared for breast tumor classification?\",\"answer\":\"The document compares logistic regression, support vector machines, random forest, decision trees, and deep neural networks for classifying tumors as benign or malignant.\"},{\"question\":\"How does the study handle class imbalance in benign vs malignant categories?\",\"answer\":\"It applies the Synthetic Minority Oversampling Technique (SMOTE) as a resampling method to balance the classes and address imbalance effects on model performance.\"},{\"question\":\"What performance does the best model achieve, and how is it evaluated?\",\"answer\":\"Random forest performs best with high accuracy and strong separability measured by area under the curve (AUC). After applying SMOTE, logistic regression and random forest remain the top models with high accuracy and high AUC values.\"}]","Enhancing Tumor Classification Through Machine Learning Algorithms for Breast Cancer Diagnosis - Research | PDF",1785807998,38,{"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},"enhancing-tumor-classification-through-machine-learning-algorithms-for-breast-cancer-diagnosis-research","",{"@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/enhancing-tumor-classification-through-machine-learning-algorithms-for-breast-cancer-diagnosis-research/121954/",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},"Which machine learning algorithms are compared for breast tumor classification?","Question",{"text":75,"@type":76},"The document compares logistic regression, support vector machines, random forest, decision trees, and deep neural networks for classifying tumors as benign or malignant.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study handle class imbalance in benign vs malignant categories?",{"text":80,"@type":76},"It applies the Synthetic Minority Oversampling Technique (SMOTE) as a resampling method to balance the classes and address imbalance effects on model performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance does the best model achieve, and how is it evaluated?",{"text":84,"@type":76},"Random forest performs best with high accuracy and strong separability measured by area under the curve (AUC). 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