[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117936-en":3,"doc-seo-117936-105":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},117936,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",7,"Healthcare","Machine Learning Technique for Prediction of Breast Cancer","Breast cancer is a leading cause of death among middle-aged women, and early detection is essential to improve recovery chances while reducing mortality. Although surgical removal and chemotherapy can be effective, the process is costly and depends on identifying tumors at an early or primary stage. This work motivates applying machine learning to medical imaging to enhance tumor finding accuracy. It introduces an automated MLTPBC system that removes irrelevant regions, reduces noise, extracts diagnostic features, and aims to predict breast cancer with improved accuracy over existing approaches.","Machine Learning Technique for Prediction of  \nBreast Cancer  \nP. Narasimhaiah1, C. Nagaraju2  \n1Research Scholar: Department ofCSE  \nYSR Engineering College of YVU  \nProddatur, India  \n[e-mail:narasimhareddypolu@gmail.com](e-mail:narasimhareddypolu@gmail.com)  \n2Professor: Department ofCSE  \nYSR Engineering College of YVU  \nProddatur, India  \n[e-mail](e-mail:cnrcse@yahoo.com)[:](e-mail:cnrcse@yahoo.com)[cnrcse@yahoo.com](e-mail:cnrcse@yahoo.com)  \nAbstract-Breast cancer is one of the most prominent disease and the second foremost source of death among middle-aged women in the world.  \nRemoving of breast tumour by using a surgical treatment and chemotherapy could work excellently if it can be identified as a primary tumour or at an early stage of transmutation, however it is a costly process. The quick development of machine learning techniques continues to burn  \nthe medical tomography enthusiasm in implementing to improve the accurateness of tumour findings. To identify breast cancer in the area of machine learning lots of attempts were made, but these techniques are not too accurate. In the proposed Machine Learning Technique for Prediction of Breast Cancer (MLTPBC) is an automated system used to remove a label, pectoral muscles, noise, and identification of cancer. The experimental results of the proposed MLTPBC shows the preferable accuracy over the existing methods.  \nKeywords: Cancer, Canny, Tomography, Mammogram, Pectoral, Features.  \nI. INTRODUCTION  \nA. Motivation  \nBreast tumour is the most prominent disease and second foremost instigation of death among middle-aged women in the world. This disease is one of the main instigations of loss of life among 45 to 55 years aged women [13] . The prevalence of breast tumours is nearly 1 in 9 women, necessitating most of the time removing the tissue completely by surgery, drug therapy, radiotherapy, and hormonal therapy [14] . The quality of the patient life is affected by the tumour to varying degrees. The main problem with this is the mental and emotional impacts ofthe disease, stress, pain, diagnostic and therapeutic measures, depression and the effects of the disease on family, conjugal and social relationships, financial burdens, nutritional problems, and treatment. The dying risk due to breast cancer is 1 in 35 [10] . The main objective of medical and therapeutic care is to better the quality of life of cancer victims. To reduce mortality, as well as promote chances of recovery is possible only if the tumour is detected and cared for from the early stages of its appearance [8] .  \nB. Literature Review  \nThe speedy progress of machine learning and deep learning [3] techniques endure to coal the cathartic tomography society's passion to implement these methods to enhance the efficiency of cancer detection [1] . Breast tumour  \nprevention's main objective is the early detection of malignancy. The breast malignancy removed by surgery and chemotherapy works effectively, if it is diagnosed as a primary tumour or at an early stage of metastasis. A widely used screening tool for breast cancer detection that helps to reduce mortality effectively is mammography [12, 22] . Highresolution images of the mammary glands are produced by using low-energy x-rays mammography as a screening method. Mortality is declining due to early detection and modern medical therapies. Other than screening methods, that have been applied to study breast tumours in the last decade was Magnetic Resonance Imaging (MRI), which was oversensitive than mammography [7 , 9] . Breast tumour screening was recommended first by Professor Forrest and more than 70% of women (ages 50 to 74 years) in the United States underwent mammography every two years [11] . Mammograms are manually examined by a radiologist to identify the presence of benign or malignant tissue in the breast. Manual examination of the mammogram of a breast by a radiologist failed several times. Having the benefits, a  \nhigh risk of false-positive a","cbCaimHXLpZ8lxYT","https://ap.wps.com/l/cbCaimHXLpZ8lxYT","pdf",1057848,1,13,"English","en",105,"# Introduction\n## Motivation\n## Literature Review\n## Proposed MLTPBC Approach","[{\"question\":\"Why is early breast cancer detection important?\",\"answer\":\"Early detection enables timely care and improves recovery chances, which helps reduce mortality. It also limits the costly and intensive outcomes that occur when tumors are identified at later stages.\"},{\"question\":\"What problems does manual mammogram examination face?\",\"answer\":\"Manual radiology review can lead to false positives and false negatives, causing benign tissue to be classified as malignant or vice versa. This increases patient risk and may require additional expensive tests such as biopsies.\"},{\"question\":\"What is MLTPBC and what does it do?\",\"answer\":\"MLTPBC is an automated system intended to remove irrelevant regions, reduce noise, and perform cancer identification using machine learning. Experimental results report preferable accuracy compared with existing methods.\"}]","Machine Learning Technique for Prediction of Breast Cancer | PDF",1785680440,33,{"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},"machine-learning-technique-for-prediction-of-breast-cancer","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-technique-for-prediction-of-breast-cancer/117936/",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","2026-08-02",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},"Why is early breast cancer detection important?","Question",{"text":75,"@type":76},"Early detection enables timely care and improves recovery chances, which helps reduce mortality. It also limits the costly and intensive outcomes that occur when tumors are identified at later stages.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problems does manual mammogram examination face?",{"text":80,"@type":76},"Manual radiology review can lead to false positives and false negatives, causing benign tissue to be classified as malignant or vice versa. This increases patient risk and may require additional expensive tests such as biopsies.",{"name":82,"@type":73,"acceptedAnswer":83},"What is MLTPBC and what does it do?",{"text":84,"@type":76},"MLTPBC is an automated system intended to remove irrelevant regions, reduce noise, and perform cancer identification using machine learning. 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