[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126991-en":3,"doc-seo-126991-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126991,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","Breast Cancer Prediction Using Machine Learning Algorithms - Report on Supervised ML Models and Evaluation","Breast cancer is a growing global health concern, making early detection and timely intervention essential for improving patient outcomes and reducing healthcare burdens. This report develops an accurate breast cancer prediction approach using supervised machine learning on the Breast Cancer Wisconsin (Diagnostic) dataset from the UCI ML repository. Logistic Regression, Decision Tree, Random Forest, SVM, Naive Bayes, and KNN are implemented in Jupyter with Python, supported by preprocessing, SMOTE, and PCA feature reduction. Model performance is assessed with accuracy, precision, recall, F1-score, and execution time across multiple train-test splits, showing strong results particularly for SVM and Logistic Regression.","Breast Cancer Prediction Using Machine Learning Algorithms  \nby  \nZeeshan Ali Shahzad  \nUniversity of Engineering and Technology  \nB. Sc. Electrical and Computer Engineering, 2012  \nA Report Submitted in Partial Fulfilment of the Requirements for the Degree of  \nMaster of Engineering  \nIn the Department of Electrical and Computer Engineering  \n©Zeeshan Ali Shahzad, 2024  \nUniversity of Victoria  \nAll rights reserved. This report may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author.  \nBreast Cancer Prediction Using Machine Learning Algorithms  \nby  \nZeeshan Ali Shahzad  \nUniversity of Victoria  \nSupervisory Committee  \nDr. T. Aaron Gulliver, Supervisor  \nDepartment of Electrical and Computer Engineering  \nDr. Mihai Sima, Departmental Member  \nDepartment of Electrical and Computer Engineering  \nAbstract  \nBreast cancer has become a pressing global health issue with its prevalence increasing worldwide. The rise in breast cancer cases is a cause for concern as it not only affects the physical and emotional well-being of individuals but also places a significant burden on the healthcare system. Early detection and timely intervention are critical factors in effectively combatting this disease. The ability to predict and diagnose breast cancer at its earliest stages can have a profound difference in patient outcomes, potentially saving countless lives. In recent years, the importance of Machine Learning (ML) in the field of healthcare has become paramount. This study considers the utility of supervised ML models to address the challenges posed by breast cancer using the publicly available Breast Cancer Wisconsin (Diagnostic) dataset from the University of California Irvine (UCI) ML repository. The Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naive Bayes and K-Nearest Neighbors (KNN) classifiers are implemented using Jupyter Notebook with Python programming.  \nThe goal of the proposed methodology is accurate breast cancer prediction. First, data preprocessing is employed to clean the dataset by removing null values and duplicates, and handling missing data. In order to balance the target labels of the dataset, Synthetic Minority Oversampling Technique (SMOTE) is employed. Then, Principal Component Analysis (PCA) is used to reduce the dimensions of the dataset. The number of components is varied (n=2, 5, 10, 15) . For training and testing the ML models, five data splits, namely 80/20, 70/30, 50/50, 30/70, and 20/80 are employed to assess the impact on model performance.  \nThe performance of the models is evaluated using the metrics accuracy, precision, recall, F1-score, and execution time. The results obtained show that SVM and Logistic Regression outperform the other models with SVM having an accuracy of 98.2% and an execution time of 9.99 ms with an 80/20 split using 10 features and Logistic Regression having an accuracy of 97.9% and an execution time of 8.42 ms with a 50/50 split using 15 features.  \nTable of Contents  \nSupervisory Committee................................................................................................................... ii  \nAbstract .......................................................................................................................................... iii  \n[List of Figures................................................................................................................................ vi](List of Figures................................................................................................................................ vi)  \n[List of Tables ...............................](List of Tables ...............................)................................................................................................. vii  \nGlossary .......................................................................................................................................","cbCaip88P5Z80Pkz","https://ap.wps.com/l/cbCaip88P5Z80Pkz","pdf",965260,5,1,65,"English","en",105,"# Supervisory Committee\n# Abstract\n# List of Figures\n# List of Tables\n# Glossary\n# Acknowledgment\n# Dedication\n# Chapter 1 Introduction\n## Motivation\n## Objectives\n## Contributions\n## Related Work\n## Outline\n# Chapter 2 Machine Learning\n## Supervised Learning\n## Unsupervised Learning\n## Reinforcement","[{\"question\":\"Which machine learning classifiers are implemented for breast cancer prediction?\",\"answer\":\"Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN) are implemented using Jupyter Notebook with Python.\"},{\"question\":\"What preprocessing steps are applied before training the models?\",\"answer\":\"The dataset is cleaned by removing null values and duplicates, handling missing data, balancing labels with SMOTE, and reducing dimensionality using PCA with varying numbers of components.\"},{\"question\":\"How is model performance evaluated and what models perform best?\",\"answer\":\"Models are evaluated using accuracy, precision, recall, F1-score, and execution time across multiple train-test splits. SVM and Logistic Regression show the strongest results, with SVM reaching 98.2% accuracy and Logistic Regression reaching 97.9% accuracy in the reported experiments.\"}]","Breast Cancer Prediction Using Machine Learning Algorithms - Report on Supervised ML Models and Evaluation | PDF",1785936071,164,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"breast-cancer-prediction-using-machine-learning-algorithms-report-on-supervised-ml-models-and-evaluation","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/breast-cancer-prediction-using-machine-learning-algorithms-report-on-supervised-ml-models-and-evaluation/126991/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Which machine learning classifiers are implemented for breast cancer prediction?","Question",{"text":77,"@type":78},"Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN) are implemented using Jupyter Notebook with Python.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What preprocessing steps are applied before training the models?",{"text":82,"@type":78},"The dataset is cleaned by removing null values and duplicates, handling missing data, balancing labels with SMOTE, and reducing dimensionality using PCA with varying numbers of components.",{"name":84,"@type":75,"acceptedAnswer":85},"How is model performance evaluated and what models perform best?",{"text":86,"@type":78},"Models are evaluated using accuracy, precision, recall, F1-score, and execution time across multiple train-test splits. SVM and Logistic Regression show the strongest results, with SVM reaching 98.2% accuracy and Logistic Regression reaching 97.9% accuracy in the reported experiments.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]