[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123884-en":3,"doc-seo-123884-105":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},123884,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Utilizing LASSO for Breast Cancer Prediction - A Hyper Machine Learning Technique with Significant - Article 22","Breast cancer is a highly prevalent and life-threatening disease that requires accurate detection and prediction to support healthy outcomes. This study develops a classification approach that targets improved performance by combining multiple machine learning classifiers with LASSO-based feature selection. Model quality is evaluated through precision, accuracy, recall, F1 score, and ROC-AUC metrics. Results indicate that the proposed SVM combined with LASSO achieves the highest accuracy, supporting more reliable breast cancer classification.","Information Sciences Letters  \n\n| Volume 12\u003Cbr>Issue 12 Dec. 2023 | Article 22 |\n| --- | --- |\n| 2023\u003Cbr>Utilizing LASSO for Breast Cancer Prediction: A Hyper Machine Learning Technique with Significant\u003Cbr>Rawia Elarabi\u003Cbr>College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia, [relarbi@jazanu.edu.sa](relarbi@jazanu.edu.sa)\u003Cbr>Najla Babiker\u003Cbr>College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia, [relarbi@jazanu.edu.sa](relarbi@jazanu.edu.sa)\u003Cbr>Awatef Balobaid\u003Cbr>College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia, [relarbi@jazanu.edu.sa](relarbi@jazanu.edu.sa)\u003Cbr>[Walaa M. Abd-Elhafiez](Walaa M. Abd-Elhafiez)\u003Cbr>College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia\\\\ Computer Science Department, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, Egypt, [relarbi@jazanu.edu.sa](relarbi@jazanu.edu.sa)\u003Cbr>Follow this and additional works at: [https://digitalcommons.aaru.edu.jo/isl](https://digitalcommons.aaru.edu.jo/isl) |  |\n\nRecommended Citation  \nElarabi, Rawia; Babiker, Najla; Balobaid, Awatef; and M. Abd-Elhafiez, Walaa (2023) \"Utilizing LASSO for Breast Cancer Prediction: A Hyper Machine Learning Technique with Significant,\" Information Sciences  \nLetters: Vol. 12 : Iss. 12 , PP-.  \nAvailable at: [https://digitalcommons.aaru.edu.jo/isl/vol12/iss12/22](https://digitalcommons.aaru.edu.jo/isl/vol12/iss12/22)  \nThis Article is brought to you for free and open access by Arab Journals Platform. It has been accepted for inclusion in Information Sciences Letters by an authorized editor. The journal is hosted on Digital Commons, an Elsevier platform. For more information, please contact [rakan@aaru.edu.jo](rakan@aaru.edu.jo), [marah@aaru.edu.jo](marah@aaru.edu.jo),  \n[u.murad@aaru.edu.jo](u.murad@aaru.edu.jo).  \nInformation Sciences Letters  \nAn International Journal  \n[http://dx.doi.org/10.18576/isl/121223](http://dx.doi.org/10.18576/isl/121223)  \nUtilizing LASSO for Breast Cancer Prediction: A Hyper Machine Learning Technique with Significant  \nRawia Elarabi1,*, Najla Babiker1, Awatef Balobaid1 and Walaa M. Abd-Elhafiez1,2  \n1College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia 2Computer Science Department, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, Egypt Received: 30 Oct. 2023, Revised: 28 Nov. 2023, Accepted: 29 Nov. 2023.  \nPublished online: 1 Dec. 2023.  \nAbstract: Cancer is a dangerous disease that greatly impacts people's lives, with breast cancer being the most common form in women. Detecting and predicting cancer accurately is crucial for a healthy life. This paper aims to achieve the highest accuracy in classifying breast cancer using various classifiers. Machine learning models and LASSO featureselection were employed, and the performance of different classifiers was compared using precision, accuracy, recall, F1 score, and ROC-AUC metrics. The results showed that the proposed model with SVM and LASSO achieved the highest accuracy.  \nKeywords: Breast cancer; Random Forest Classifier; Support Vector Machines; AdaBoost Classifier; classification; machine learning, KNN.  \n1 Introduction  \nBreast cancer can be another critical factor in the death of women. According to the World Health Organization site, around 685,000 people around the globe died in 2020 because of breast cancer, affecting 2.3 million women. Breast cancer was the most common public cancer on the globe at the end of 2020, having been identified in 7.8 million women in the prior five years. Malignant and benign tumors can be distinguished to diagnose this disease [1] [2] [3] . Tumors needed an accurate diagnosis to differentiate between malignant and benign tumors. Breast cancer has four stages that vary from stage 0 to stage 4. Stage 0 breast cancer is a norm","cbCaij4yRgtx5Y1M","https://ap.wps.com/l/cbCaij4yRgtx5Y1M","pdf",1254428,1,14,"English","en",105,"# Introduction\n## Related work\n## Dataset and methods","[{\"question\":\"What is the main goal of this paper?\",\"answer\":\"To classify breast cancer with the highest possible accuracy by comparing multiple classifiers combined with LASSO feature selection.\"},{\"question\":\"How do the authors improve the prediction model?\",\"answer\":\"They use LASSO for feature selection to identify highly associated characteristics and to help address overfitting and underfitting.\"},{\"question\":\"Which model performs best in the results?\",\"answer\":\"The proposed SVM model with LASSO achieves the highest accuracy based on the reported evaluation metrics.\"}]","Utilizing LASSO for Breast Cancer Prediction - A Hyper Machine Learning Technique with Significant - Article 22 | PDF",1785819064,35,{"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},"utilizing-lasso-for-breast-cancer-prediction-a-hyper-machine-learning-technique-with-significant-article-22","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/utilizing-lasso-for-breast-cancer-prediction-a-hyper-machine-learning-technique-with-significant-article-22/123884/",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},"What is the main goal of this paper?","Question",{"text":75,"@type":76},"To classify breast cancer with the highest possible accuracy by comparing multiple classifiers combined with LASSO feature selection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the authors improve the prediction model?",{"text":80,"@type":76},"They use LASSO for feature selection to identify highly associated characteristics and to help address overfitting and underfitting.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best in the results?",{"text":84,"@type":76},"The proposed SVM model with LASSO achieves the highest accuracy based on the reported evaluation metrics.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]