[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124785-en":3,"doc-seo-124785-105":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":4,"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},124785,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Identification and Diagnosis of Breast Cancer - Using Different Machine Learning Algorithms - On Coimbra Dataset - Abstract and Introduction","Cancer is the most deadly disease worldwide, and breast cancer is the second-most common cancer among women globally. Many patients face limited access to medical support, so early diagnosis is especially difficult in developing regions. Earlier detection enables timely clinical therapy, improving survival rates and supporting intervention before later-stage progression. This study uses the Coimbra dataset from UCI with nine input features and a binary class label to apply supervised machine learning and evaluate performance for stage diagnosis.","Identification and Diagnosis of Breast Cancer At Different Stages By Different Machine Learning Algorithms On The Coimbra Dataset  \nManish Tiwari1, Nagendra Singh*2, Harsh Pratap Singh3, Lokendra Singh Songare4, Pinky Rane5, Shubha Soni6, Rakesh  \nPandit7  \n1Department of computer science engineering,  \nCareer point university,  \nKota, Rajasthan  \n[immanishtiwari@gmail.com](immanishtiwari@gmail.com)  \n2Department of Electrical Engineering,  \nTrinity College of Engineering and Technology,  \nKarimnagar, Telangana, India  \n[nsingh007@rediffmail.com](nsingh007@rediffmail.com)  \n3Department of Computer Science and Engineering,  \nShri Vaishnav Vidyapeeth Vishwavidyalaya,  \nIndore MP, India,  \n[drharshprataps@gmail.com](drharshprataps@gmail.com)  \n4CSE Department,  \nMedi-Caps University, Indore,  \n[lokendra.songare@gmail.com](lokendra.songare@gmail.com)  \n5Department Computer Science and Engineering,  \nMedi-Caps University,  \nIndore, MP, India  \n[pink.shinde@gmail.com](pink.shinde@gmail.com)  \n6Department Computer Science and Engineering,  \nInstitute of Engineering and Technology Sagar, M.P,  \n[shubhasoni01@gmail.com](shubhasoni01@gmail.com)  \n7Department Computer Science and Engineering,  \nMedi-Caps University,  \nIndore, MP, India  \n[rakesh.pandit@medicpas.ac.in](rakesh.pandit@medicpas.ac.in)  \n*Corresponding Authors : Nagendra Singh, [Email : nsingh007@rediffmail.com](Email : nsingh007@rediffmail.com)  \nAbstract—Cancer is the most deadly disease in the world. Breast cancer is the second-most common disease in women worldwide. It is the most common cancer globally among women. Annually, 12.5% of all new cancer cases worldwide Globally, 2.26 million breast cancers were discovered, and 685,000 women died from this disease. Early diagnosis of breast cancer is more difficult in developing countries than in developed countries. Using technology, if it is possible to detect cancer early and treat it on time, then many women can be cured and their lives can be saved. Early detection also leads to an increased survival rate for patients who receive clinical therapy before reaching later stages. It includes a number of risk factors, such as modifiable and non-modifiable ones. A recent survey discovered that for women above 50 years of age, the chance of getting breast cancer is about 80%. Machine learning algorithms are playing a major role in diagnosing liver cancer in its early stages and helping doctors make prompt decisions. A number of machine learning models have been executed in which the model gave better performance in terms of accuracy, and other parameters such as precision, recall, etc. are used to predict early. In this research work, the latest dataset, Coimbra, belongs to UCI machinery. It has nine features (age, BMI, glucose, insulin, HOMA, leptin, adiponectin, Resistin, MCP.1) and  \none classification attribute, which has values 1 and 2. 1 belongs to benign, and 2 belongs to malignant. Based on that, the supervised machine learning algorithm was applied. The WEKA tool is used to analyze the dataset. A number of algorithms are applied, such as Bayes net, multilayer perceptron, IBK, random committee, random tree, etc. More of them gave better results, and that model was chosen as the key model for breast cancer analysis.  \nKeywords-Cancer, breast cancer, deep learning, WEKA tool and detection of stage of cancer  \nI. INTRODUCTION  \nIn the human body, when cell growth occurs uncontrollably and slowly spreads to other parts of the body, cancer disease arises. Cancer may happen anywhere in the human body. But in the case of women Breast cancer is a very common problem. In the last decade, breast cancer has been the most responsible for the deaths of women compared to other types of cancer. As per the World Health Organization (WHO), many millions of women were diagnosed with breast cancer, and out of them, many died due to a lack of medical support. The main problem is the identification of cancer in its early stages. In most case","cbCaimpTYasaUmfk","https://ap.wps.com/l/cbCaimpTYasaUmfk","pdf",500443,1,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What problem does the document focus on?\",\"answer\":\"The document focuses on identifying and diagnosing breast cancer at different stages, especially emphasizing the importance of early detection for better outcomes.\"},{\"question\":\"What dataset and attributes are used for the study?\",\"answer\":\"It uses the Coimbra dataset from UCI, which contains nine features (e.g., age, BMI, glucose, insulin and related lab measures) and a classification attribute with values 1 (benign) and 2 (malignant).\"},{\"question\":\"Which machine learning tools and algorithms are applied?\",\"answer\":\"The WEKA tool is used to analyze the dataset, and multiple supervised machine learning algorithms are evaluated, including Bayes net, multilayer perceptron, IBK, random committee, and random tree.\"}]","Identification and Diagnosis of Breast Cancer - 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