[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119224-en":3,"doc-seo-119224-105":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},119224,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","An Efficient Breast Cancer Detection Using Machine Learning Classification Models","Breast cancer remains a dangerous and common global disease, making early identification critical for improving patient outcomes. Recent machine learning advances deliver higher accuracy and efficiency across many application areas, supporting more reliable diagnostic assistance. This study proposes a machine-learning framework to raise breast cancer detection accuracy by combining advanced feature selection, dependable classification models, and improved training with thorough hyperparameter tuning. Random forest and gradient boosting provide the best results, reaching 97.90% accuracy and an ROC of 0.99, demonstrating strong potential for diagnosis and prognosis.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 20 No. 13 (2024) |   \n[https://doi.org/10.3991/ijoe.v20i13.50289](https://doi.org/10.3991/ijoe.v20i13.50289)  \nPAPER  \nAn Efficient Breast Cancer Detection Using Machine Learning Classification Models  \nB. N. Ravi Kumar1, Naveen Chandra Gowda2, B. J. Ambika3(*),  \nH. N. Veena4, B. Ben Sujitha5, D. Roja Ramani6  \n1Department of Information Science and Engineering, BMS Institute of Technology and Management, Bengaluru, Karnataka, India  \n2School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India  \n3Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India  \n4Department of Computer Science and Engineering, SJB Institute of Technology, Bengaluru, Karnataka, India  \n5Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kanyakumari, Tamil Nadu, India  \n6Department of Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India  \n[ambika.bj@manipal.edu](ambika.bj@manipal.edu)  \nABSTRACT  \nBreast cancer is still a dangerous and common disease that affects women all over the world, which highlights how crucial early identification is to better patient outcomes. In recent years, utilizing machine learning (ML) algorithms has improved accuracy and efficiency dramatically in a variety of applications, showing promising outcomes. This article provides a novel machine-learning approach to increase the accuracy of breast cancer detection. To improve diagnostic efficiency and accuracy, our suggested methodology combines sophisticated feature selection strategies, reliable classification algorithms, and enhanced model training methodologies. We investigated several ML classifiers, and after thorough hyperparameter tuning, the models were. Random forest and gradient boosting have achieved the highest performance with an accuracy of 97.90% and an ROC score of 0.99. This research highlights the effectiveness of ML, particularly the random forest algorithm, in breast cancer diagnosis and prognosis. Future work may explore deep learning techniques for determining the disorder’s severity.  \nKEYWORDS  \nwomen health, breast cancer, machine learning (ML), classification algorithms  \n1 INTRODUCTION  \nA worldwide epidemic, cancer affects a wide range of populations. Because breast cancer affects many women, research on diagnosis and prognosis needs to be concentrated. Machine learning (ML) for early prediction is promising. It is second in female mortality after lung cancer and is frequently associated with advanced age. It is caused by abnormal proliferation of breast cells [1] . Breast cancer, a multifaceted ailment, ranks as the most prevalent cancer among women globally [2] . Roughly 30% of female cancer cases stem from it, with 1.5 million women diagnosed annually, causing 500,000 deaths worldwide [3] . Despite its rising incidence over three decades, mortality has declined, attributed 20% to mammography screening and 60% to enhanced cancer therapies [4] .  \nRavi Kumar, B.N., Gowda, N.C., Ambika, B.J., Veena, H.N., Sujitha, B.B., Ramani, D.R. (2024). An Efficient Breast Cancer Detection Using Machine Learning Classification Models. International Journal of Online and Biomedical Engineering (iJOE), 20(13), pp. 24–40. [https://doi.org/10.3991/ijoe.v20i13.50289](https://doi.org/10.3991/ijoe.v20i13.50289)[ ](https://doi.org/10.3991/ijoe.v20i13.50289)[Article submitted 2024-05-28. Revision uploaded 2024-07-19. Final acceptance 2024-07-19.](Article submitted 2024-05-28. Revision uploaded 2024-07-19. Final acceptance 2024-07-19.)  \n© 2024 by the authors of this article. Published under CC-BY.  \n24 International Journal of Online and Biomedical Enginee","cbCaioK1x6ju8CmC","https://ap.wps.com/l/cbCaioK1x6ju8CmC","pdf",835698,1,17,"English","en",105,"# Introduction\n## Background on breast cancer and screening limitations\n## Role of machine learning in early prediction","[{\"question\":\"Why is early breast cancer detection important?\",\"answer\":\"Early identification improves patient outcomes and supports timely diagnosis and intervention, addressing limitations of current screening approaches.\"},{\"question\":\"What machine learning approach does the paper propose?\",\"answer\":\"The paper proposes a framework that integrates sophisticated feature selection, classification algorithms, and enhanced model training with hyperparameter tuning.\"},{\"question\":\"Which models achieve the best performance, and what are the results?\",\"answer\":\"Random forest and gradient boosting achieve the highest performance, with 97.90% accuracy and an ROC score of 0.99.\"}]","An Efficient Breast Cancer Detection Using Machine Learning Classification Models | 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