[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122764-en":3,"doc-seo-122764-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"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},122764,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Breast Cancer Detection - Extracting and Selecting Features Using Machine Learning","Breast cancer is a leading cause of female mortality worldwide, and early screening directly supports higher survival rates. This research leverages machine learning by extracting representative features from medical data and removing irrelevant ones to reduce dimensionality without sacrificing or improving model accuracy. Mammography images are classified into benign and malignant classes using supervised methods such as support vector machines, random forests, and artificial neural networks, then compared to identify the best-performing model.","Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning  \n1,* Priyanka M. Tambat, 2Dr. Sohel A. Bhura, 3Dr. Salim Y. Amdani, 4Dr. Suresh S. Asole  \n1,3,4 Babasaheb Naik College of Engineering, Pusad, Maharashtra, India  \n2Jhulelal Institute of Technology, Nagpur, Maharashtra, India  \n[1](1 mail.priyankaaher@gmail.com)[ mail.priyankaaher@gmail.com](1 mail.priyankaaher@gmail.com), [2](2sabhura@rediffmail.com)[sabhura@rediffmail.com](2sabhura@rediffmail.com), [3](3drsalimamdani@gmail.com)[drsalimamdani@gmail.com](3drsalimamdani@gmail.com), [4](4 suresh_asole@yahoo.com)[ suresh_asole@yahoo.com](4 suresh_asole@yahoo.com)  \nAbstract: The cancer of the breast is a significant cause of female death worldwide, but especially in developing countries. For better results and higher survival rates, early diagnosis and screening are crucial. Machine learning (ML) methods can aid in the initialdiscovery and diagnosis of breast cancer by choosing the most informative elements from medical data and eliminating irrelevant ones. The approach of feature extraction involves taking unstructured data and extracting a representative set of characteristics that may be used to classify or forecast data. The aim is to decrease the dimensionality of the feature space while upholding or even refining the accuracy of the ML model. An artificial intelligence model is developed on the given features to categorize mammography images into benign and malignant groups. Different supervised learning techniques, including support vector machines, random forests, and artificial neural networks, are employed and contrasted in order to select the best-performing model. This research offers a comprehensive framework for utilizing machine learning methods to detect breast cancer. The technique demonstrates how it might assist radiologists in the early detection of breast cancer by effectively extracting and selecting critical characteristics that could improve patient outcomes and potentially save lives.  \nKeywords: Feature Selection, Feature Extraction method, Support Vector Machine, Random forest, Recursive Feature Elimination.  \nI. INTRODUCTION  \nBy extracting and choosing essential information from the patient's data, automatic learning algorithms can help doctors identify and diagnose breast cancer. Extraction and selection of features are critical steps in detecting relevant patterns and qualities in medical data[5] . Machine learning algorithms can learn to identify between malignant and non-cancerous instances by extracting useful information from multiple sources such as mammograms, clinical records, and genetic data. Feature extraction techniques convert raw data into a compact and representative set of characteristics, making classification or prediction jobs more efficient and accurate [4] .  \nBy finding the most useful and discriminative features, featureselection approaches handle the difficulty of high-dimensional data [1] . Breast cancer arises when the unregulated development of cells in the breast's fatty and fibrous tissues results in the production of malignant tumors. These malignant cells have the ability to spread beyond the primary  \ntumor, moving through increasing severity stages. Breast cancer's aggressive nature contributes to its rank as one of the most fatal diseases in contemporary times [2] .  \nBreast abnormalities can be found using a number of methods, including imaging, medical exams, and self-examination. By extracting key information from various medical datasets, machine learning algorithms can more effectively and  \nprecisely diagnose breast cancer patients [3].However, mammography has its limitations in specific situations, especially for people with dense pectoral tissue. Additionally, there is a large rise in the danger of ionizing radiation exposure, which is important for young women. Furthermore, it can be challenging to find tumors with a diameter of less than 2 mm utilizing mammography. These dr","cbCaifq8s4L77w6r","https://ap.wps.com/l/cbCaifq8s4L77w6r","pdf",412182,1,"English","en",105,"# Introduction\n# Review of Literature\n# Dataset\n# Proposed Methodology\n## Feature Extraction and Selection\n# Results\n# Conclusion","[{\"question\":\"Why is early breast cancer diagnosis important?\",\"answer\":\"Early diagnosis and screening improve survival rates by enabling timely detection of malignant disease.\"},{\"question\":\"How does the proposed method use feature extraction and selection?\",\"answer\":\"It extracts representative characteristics from medical data, reduces the dimensionality of the feature space, and removes irrelevant information to preserve or refine classification accuracy.\"},{\"question\":\"Which supervised learning models are compared for breast cancer classification?\",\"answer\":\"The study compares support vector machines, random forests, and artificial neural networks to determine the best-performing model for classifying mammography images into benign and malignant groups.\"}]","Breast Cancer Detection - 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