[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120021-en":3,"doc-seo-120021-105":30,"detail-sidebar-cat-0-en-105":90},{"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},120021,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Enhancing Tumor Classification Through Machine Learning Algorithms for Breast Cancer Diagnosis - Paper","Machine learning enables faster and more reliable breast cancer detection by supporting clinicians with an additional analytical perspective. This study evaluates five commonly used models—Logistic Regression, Support Vector Machines, Random Forest, Decision Tree, and Deep Neural Networks—on the Wisconsin breast cancer dataset to classify tumors as benign or malignant. Random Forest with cross-validation reduces features to 17 and reaches 96.84% validation, while SMOTE addresses class imbalance, yielding top performance with accuracy up to 94.70% and AUC near 0.997.","University of Texas Rio Grande Valley  \nScholarWorks @ UTRGV  \n\n| School of Mathematical and Statistical\u003Cbr>Sciences Faculty Publications and College of Sciences\u003Cbr>Presentations |\n| --- |\n| 6-30-2024\u003Cbr>Enhancing Tumor Classification Through Machine Learning Algorithms for Breast Cancer Diagnosis\u003Cbr>Lawrence Agbota\u003Cbr>The University of Texas Rio Grande Valley\u003Cbr>Edmund Agyemang\u003Cbr>The University of Texas Rio Grande Valley\u003Cbr>Priscilla Kissi-Appiah\u003Cbr>The University of Texas Rio Grande Valley\u003Cbr>Lateef Moshood\u003Cbr>The University of Texas Rio Grande Valley\u003Cbr>Akua Osei-Nkwantabisa\u003Cbr>The University of Texas Rio Grande Valley\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://scholarworks.utrgv.edu/mss_fac](https://scholarworks.utrgv.edu/mss_fac)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons, Mathematics Commons, and the Medicine and Health Sciences Commons |\n\nRecommended Citation  \nAgbota, Lawrence, Edmund Agyemang, Priscilla Kissi-Appiah, Lateef Moshood, Akua Osei-Nkwantabisa, Vincent Agbenyeavu, Abraham Nsiah, and Augustina Adjei.“Enhancing Tumor Classification Through Machine Learning Algorithms for Breast Cancer Diagnosis.” Computer Engineering and Intelligent Systems 15, no. 1 (2024): 71–85 . [https://doi.org/10.7176/CEIS/15-1-08](https://doi.org/10.7176/CEIS/15-1-08)  \nThis Article is brought to you for free and open access by the College of Sciences at ScholarWorks @ UTRGV. It has been accepted for inclusion in School of Mathematical and Statistical Sciences Faculty Publications and Presentations by an authorized administrator of ScholarWorks @ UTRGV. For more information, please contact [justin.white@utrgv.edu](justin.white@utrgv.edu), [william.flores01@utrgv.edu](william.flores01@utrgv.edu).  \nAuthors  \nLawrence Agbota, Edmund Agyemang, Priscilla Kissi-Appiah, Lateef Moshood, Akua Osei- Nkwantabisa, Vincent Agbenyeavu, Abraham Nsiah, and Augustina Adjei  \nThis article is available at ScholarWorks @ UTRGV: [https://scholarworks.utrgv.edu/mss_fac/556](https://scholarworks.utrgv.edu/mss_fac/556)  \nEnhancing 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 i","cbCaitjh3Eb8SoCi","https://ap.wps.com/l/cbCaitjh3Eb8SoCi","pdf",1752952,1,17,"English","en",105,"# Abstract\n## Introduction\n## Methods and Models\n## Results and Performance\n## Conclusion","[{\"question\":\"Which machine learning algorithms are compared for breast tumor classification?\",\"answer\":\"The study compares Logistic Regression, Support Vector Machines (SVM), Random Forest, Decision Tree, and Deep Neural Networks (DNN).\"},{\"question\":\"How is class imbalance handled in the study?\",\"answer\":\"\"},{\"question\":\"What performance does Random Forest achieve in the experiments?\",\"answer\":\"Random Forest attains a validation score of 96.84% and, after applying SMOTE, achieves an accuracy of 94.70% with an AUC of 0.997. \"}]","Enhancing Tumor Classification Through Machine Learning Algorithms for Breast Cancer Diagnosis - Paper | PDF",1785727763,43,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"enhancing-tumor-classification-through-machine-learning-algorithms-for-breast-cancer-diagnosis-paper","",{"@graph":36,"@context":84},[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/research-report/",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-paper/120021/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,80],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are compared for breast tumor classification?","Question",{"text":75,"@type":76},"The study compares Logistic Regression, Support Vector Machines (SVM), Random Forest, Decision Tree, and Deep Neural Networks (DNN).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is class imbalance handled in the study?",{"text":34,"@type":76},{"name":81,"@type":73,"acceptedAnswer":82},"What performance does Random Forest achieve in the experiments?",{"text":83,"@type":76},"Random Forest attains a validation score of 96.84% and, after applying SMOTE, achieves an accuracy of 94.70% with an AUC of 0.997.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]