[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121932-en":3,"doc-seo-121932-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121932,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Breast Cancer Classification with Machine Learning","Breast cancer represents a leading cause of death among women worldwide, with malignant tumors able to metastasize and become incurable without timely treatment. This study evaluates a Support Vector Machine (SVM) classifier on the Wisconsin Breast Cancer dataset and compares it with previously reported KNN and Naive Bayes results. The work examines the impact of kernel functions and hyperparameter tuning, using Python and scikit-learn, and assesses performance through accuracy, feature distribution, correlation analysis, and decision-boundary visualization.","Breast Cancer Classification with Machine learning  \nRahanuma Tarannum 1 , Jerry Wood2 , Tolga Ensari3 ,  \nGraduate College, Information Technology, Russellville, Arkansas Tech University Emails: 1: rtarannum@atu.edu, 2: [jwood@atu.edu](jwood@atu.edu), 3: [tensari@atu.edu](tensari@atu.edu)  \nIntroduction  \nBreast cancer is one of the foremost causes of death amongst women worldwide. Breast tumours are characteristically classified as either benign (non-cancerous) or malignant (cancerous) . Benign tumours do not spread external side of the breast and are not fatal, whereas malignant tumours can metastasize and be incurable if untreated. Rapidly and accurate diagnosis of malignant tumours is significant for efficient treatment and advanced outcomes. In 2022, breast cancer claimed 670 000 lives worldwide. Women without any particular risk factors other than age and sex account for half of all cases of breast cancer. In 157 out of 185 nations, breast cancer was the most frequent cancer among women in 2022. Worldwide, breast cancer affects people in every nation. Men are affected by breast cancer at a rate of 0.5–1%[1] . By exercising the SVM algorithm, I aim to leveraging its adeptness to efficiently handle high-dimensional data and portray complex, non-linear relationships involving features and class labels.  \nResearch Purpose  \nIn this study, I will investigate the performance of the SVM classifier on the Wisconsin Breast Cancer dataset and compare it with the formerly reported results using KNN and NB classifiers. Furthermore, I will probe the effect of different kernel functions and hyperparameter tuning on the SVM's performance to enhance its classification capabilities.  \nThe results of this research will provide perceptions into the effectiveness of the SVM algorithm for breast cancer diagnosis and influence on the development of accurate and trustworthy machine learning-based diagnostic tools.  \nMethodology  \nDatasets  \nThe Wisconsin Breast Cancer dataset from the UCI Machine Learning Repository will be utilized in this study. The dataset encompasses 569 instances of breast tumor cases, each characterized by 32 features portraying the characteristics of the tumor cell nuclei exhibit in the digitized image of a fine needle aspirate (FNA) of a breast mass.  \nMy goal is to demonstrate the potential of potent machine learning techniques in research projects; hence I’m concentrating on concepts rather than specifics. The Support Vector Machine (SVM) algorithm will be executed using the scikit-learn library in Python. Both linear and non-linear SVM classifiers will be evaluated. For non-linear SVMs, different kernel functions, such as radial basis function (RBF), polynomial, and sigmoid, will be discovered to portray the non-linear relationships involving the features and the class labels.  \nComparative Analysis and Data Exploration  \nMy study expected to evaluate the performance of the Support Vector Machine (SVM) classifieron the Wisconsin Breast Cancer dataset and to compare its effectiveness with previously reported results using K-Nearest Neighbors (KNN) and Naive Bayes (NB) classifiers. The dataset includes features such as radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension.  \nSVM implementation in Python  \nAfter splitting the datasets into training and test sets. An SVM model created by using the RBF kernel, with gamma=0.5 and C=1.0 . Which trained on the training data. The decision boundary of the SVM model was plotted, visualizing the separation between the benign and malignant samples.  \nAnalysis  \nI used the following equations- 􀟱 􀰛 􀝔 + 􀜾 = 0 where w is the normal vector to the hyperplane and b has become the bias term.  \nOptimization Problem  \nThe dual objective function is given below-  \nKernel Functions  \n| Findings and Results\u003Cbr>The distribution of the features was plotted, showing the histograms or bar plots for the first 10 features, The correl","cbCaiimCSQ3vxFIy","https://ap.wps.com/l/cbCaiimCSQ3vxFIy","pdf",296514,1,"English","en",105,"# Introduction\n# Research Purpose\n# Methodology\n## Datasets\n## Comparative Analysis and Data Exploration\n## SVM implementation in Python\n## Analysis\n# Findings and Results\n# Discussion\n## Impact of Kernel Functions and Hyperparameter Tuning\n## Visualization and Interpretation","[{\"question\":\"What dataset and features are used for the breast cancer classification study?\",\"answer\":\"The study uses the Wisconsin Breast Cancer dataset from the UCI Machine Learning Repository, containing 569 tumor instances described by 32 features extracted from digitized image characteristics of fine needle aspirates.\"},{\"question\":\"How does the SVM approach compare with KNN and Naive Bayes in reported accuracy?\",\"answer\":\"With an RBF kernel, the SVM achieves about 0.62 accuracy, while KNN and Naive Bayes report approximately 0.95 and 0.92 respectively, showing higher performance than the initial SVM setup.\"},{\"question\":\"How do kernel functions and hyperparameter tuning affect SVM performance?\",\"answer\":\"The analysis shows that changing kernels and tuning hyperparameters improves results: a linear kernel reaches about 0.96 accuracy and a polynomial kernel (degree 3) reaches about 0.95, indicating kernel choice is critical.\"}]","Breast Cancer Classification with Machine Learning | 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dataset and features are used for the breast cancer classification study?","Question",{"text":73,"@type":74},"The study uses the Wisconsin Breast Cancer dataset from the UCI Machine Learning Repository, containing 569 tumor instances described by 32 features extracted from digitized image characteristics of fine needle aspirates.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does the SVM approach compare with KNN and Naive Bayes in reported accuracy?",{"text":78,"@type":74},"With an RBF kernel, the SVM achieves about 0.62 accuracy, while KNN and Naive Bayes report approximately 0.95 and 0.92 respectively, showing higher performance than the initial SVM setup.",{"name":80,"@type":71,"acceptedAnswer":81},"How do kernel functions and hyperparameter tuning affect SVM performance?",{"text":82,"@type":74},"The analysis shows that changing kernels and tuning hyperparameters improves results: a linear kernel reaches about 0.96 accuracy and a polynomial kernel (degree 3) reaches 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