{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/on-breast-cancer-detection-an-application-of","title":"On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset","arxiv_id":"1711.07831","date":"2017-11-20","proceeding":null,"authors":["Abien Fred Agarap"],"abstract":"This paper presents a comparison of six machine learning (ML) algorithms:\nGRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest\nNeighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on\nthe Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &\nMangasarian, 1992) by measuring their classification test accuracy and their\nsensitivity and specificity values. The said dataset consists of features which\nwere computed from digitized images of FNA tests on a breast mass (Wolberg,\nStreet, & Mangasarian, 1992). For the implementation of the ML algorithms, the\ndataset was partitioned in the following fashion: 70% for training phase, and\n30% for the testing phase. The hyper-parameters used for all the classifiers\nwere manually assigned. Results show that all the presented ML algorithms\nperformed well (all exceeded 90% test accuracy) on the classification task. The\nMLP algorithm stands out among the implemented algorithms with a test accuracy\nof ~99.04%.","url_abs":"http://arxiv.org/abs/1711.07831v4","url_pdf":"http://arxiv.org/pdf/1711.07831v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"on-breast-cancer-detection-an-application-of","repo_url":"https://github.com/AFAgarap/wisconsin-breast-cancer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1711.07831","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}