[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122689-en":3,"doc-seo-122689-105":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":20,"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},122689,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Breast Cancer Detection Problem - Using Various Machine Learning Techniques - Health Prediction Context","Breast cancer is a common disease with potentially severe outcomes, making early diagnosis and timely lifestyle guidance essential to slow progression. A breast cancer detection and prediction system is developed using machine learning models including SVM, NB, and AdaBoost. The study targets automated risk assessment with reduced error rates by learning patterns from medical data, supporting clinicians in identifying high-risk patients and improving diagnostic decisions while raising ethical and confidentiality requirements for sensitive data.","A Breast Cancer Detection Problem using various Machine Learning Techniques in the Context of Health Prediction System  \nRAFALIA Najat 1 , ETTAZI Haitam 1 , ABOUCHABAKA Jaafar1 1Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco  \nAbstract. Today, breast cancer is one of the most common diseases that  \ncan cause certain complications, sometimes worst-case scenario is death.  \nThus, there is an urgent need for a diagnosis tool that can help doctors  \ndetect the disease at an early stage and recommend the necessary lifestyle  \nchanges to stop the progression of the disease; the likelihood of developing  \ncancer at a young age has also been greatly increased by environmental  \nchanges in our everyday lives. Machine learning is an urgent need today to  \nenhance human effort and offer higher automation with fewer errors. In  \nthis article, a breast cancer detection and prediction system is developed  \nbased on machine learning models (SVM, NB, AdaBoost) . The achieved  \naccuracies of the developed models are as follows: SVM achieved an  \noverall score of 98. 82%, NB achieved an overall score of 97.71%, and  \nfinally, AdaBoost achieved an overall score of 97.71% .  \nIndex Terms— Machine Learning, NB , Environmental changes SVM,  \nAdaBoost , breast cancer, detection, prediction.  \n1 Introduction  \nDistinct habitat types have distinct effects on a range of cancer risk factors (exposure to UV radiation, pollution, and habitat fragmentation) . Machine learning has been used to map different habitat types in different ecosystems, as well as human-caused environmental deterioration such nuclear fallout, hydrocarbon spill pollution, and habitat fragmentation.  \nThe increasing application of machine learning in habitat mapping will advance our understanding of how cancer affects various organisms and landscapes.  \nDepending on the kind of habitat (exposure to ultraviolet radiation, pollution, and habitat fragmentation), a number of cancer risk variables are affected differently. In addition to mapping different habitat types across different ecosystems, machine learning has also been used to map environmental degradation brought on by humans, such as habitat fragmentation and contamination from petroleum spills. Research on how cancer affects species and ecosystems will be improved by the growing application of machine learning in habitat mapping.  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nThe use of machine learning in disease prediction has become increasingly popular in the medical and health fields due to its ability to process large amounts of data quickly and efficiently. By using precise techniques to extract patterns from medical data such as genomic data, patient data, and medical images, machine learning can assist healthcare professionals in identifying risk factors, diagnosing diseases, and predicting their development. These models can be customized to predict the likelihood of a patient developing a particular disease, identify risk factors, diagnose diseases, predict the course of a disease, and even help select the best treatment for a particular patient.  \nBreast cancer is a serious and prevalent disease that affects millions of women worldwide. Early detection and diagnosis of breast cancer is critical for improving a patient's chances of recovery and clinical outcomes. Machine learning algorithms can be applied to extract significant features from medical data, such as mammography images, breast tissue biopsies, and patient history. By analysing this data, an accurate prediction model can be developed to estimate the probability of a patient developing breast cancer. These models can also help identify high-risk patients who may require closer monitoring or earlier screening.  \nHowever, it is important to note","cbCaidENczF85zih","https://ap.wps.com/l/cbCaidENczF85zih","pdf",538868,1,11,"English","en",105,"# Introduction\n# Related works","[{\"question\":\"What is the main goal of the proposed system?\",\"answer\":\"To detect breast cancer at an early stage and support prediction through a machine learning-based detection and prediction system.\"},{\"question\":\"Which machine learning models are used in the study?\",\"answer\":\"The study develops models based on SVM, NB, and AdaBoost.\"},{\"question\":\"What accuracy results are reported for the models?\",\"answer\":\"SVM reaches an overall score of 98.82%, while NB and AdaBoost reach an overall score of 97.71%.\"}]","Breast Cancer Detection Problem - Using Various Machine Learning Techniques - Health Prediction Context | PDF",1785812246,28,{"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},"breast-cancer-detection-problem-using-various-machine-learning-techniques-health-prediction-context","",{"@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/breast-cancer-detection-problem-using-various-machine-learning-techniques-health-prediction-context/122689/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the proposed system?","Question",{"text":75,"@type":76},"To detect breast cancer at an early stage and support prediction through a machine learning-based detection and prediction system.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used in the study?",{"text":80,"@type":76},"The study develops models based on SVM, NB, and AdaBoost.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy results are reported for the models?",{"text":84,"@type":76},"SVM reaches an overall score of 98.82%, while NB and AdaBoost reach an overall score of 97.71%.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]