[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123024-en":3,"doc-seo-123024-105":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},123024,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine learning techniques to identify risk factors of breast cancer among women in Mashhad, Iran - research summary","Machine learning techniques are applied to identify key breast cancer risk factors among women in Mashhad, Iran, addressing the challenge of low survival rates in developing settings where early detection and adequate diagnosis are limited. A case-control design analyzes two datasets comprising 516 and 606 samples, using Decision Tree, Random Forest, Logistic Regression, and Principal Component Analysis in RStudio. Results highlight distinct feature sets across models, supporting algorithm-driven extraction of actionable risk factors relevant to Iran.","J PREV MED HYG 2024; 65: E221-E226  \nOPEN ACCESS  \nNoN comuNicable disease  \nMachine learning techniques to identify risk factors of breast cancer among women in Mashhad, Iran  \nATIEH KHALEGHI1, SEYYED MOHAMMAD TABATABAEI2,3, ZEINAB SADAT HOSSEINI4, MOSLEM TAHERI SOODEJANI5,  \nEHSAN MOSA FARKHANI6, MARYAMYAGHOOBI3,7  \n1 Data Scientist at AFRY AB, Sweden; 2 Department of Medical Informatics, Faculty of Health Mashhad University of Medical Sciences, Mashhad, Iran; 3 Clinical Research Development Unit, Imam Reza Hospital, University of Medical Sciences, Mashhad, Iran; 4 Faculty of Medicine, Islamic Azad University of Mashhad, Mashhad, Iran; 5 Research Center of Prevention and Epidemiology of Non-Communicable Disease, Department of Biostatistics and Epidemiology, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; 6 Department of Epidemiology, Faculty of Health Mashhad University of Medical Sciences, Mashhad, Iran;  \n7 Department of Epidemiology, Faculty of Public Health, Iran University of Medical Sciences, Tehran, Iran  \nKeywords  \nRandom forest • Logistic regression • Decision tree • Principal component analysis • Breast cancer  \nSummary  \nBackground. Low survival rates of breast cancer in developing countries are mainly due to the lack of early detection plans and adequate diagnosis and treatment facilities.  \nObjectives. This study aimed to apply machine learning techniques to recognize the most important breast cancer risk factors. Methods. This case-control study included women aged 17-75 years who were referred to medical centers affiliated with Mashhad University of Medical Science between March 21, 2015, and March 19, 2016. The study had two datasets: one with 516 samples (258 cases and 258 controls) and another with 606 samples (303 cases and 303 controls). Written informed consent has been observed. Decision Tree (DT), Random Forest (RF), Logistic  \nRegression (LR), and Principal Component Analysis (PCA) were applied using R studio software.  \nResults. Regarding the DT and RF, the most important features that impact breast cancer were family cancer, individual history of breast cancer, biopsy sampling, rarely consumption of a dairy, fruit, and vegetable meal, while in PCA and LR these features including family cancer, pregnancy number, pregnancy tendency, abortion, first menstruation, the age of first childbirth and childbirth number.  \nConclusions. Machine learning algorithms can be used to extract the most important factors in the diagnosis of breast cancer in developing countries such as Iran.  \nIntroduction  \nThe most prevalent malignancy of women in the world is breast cancer [1] . Globally, there were 2.3 million new instances of breast cancer in 2020, and this disease claimed 685,000 lives [2]. By 2040, it is expected that there will be more than 3 million new cases and 1 million deaths due to breast cancer worldwide [3] . Breast cancer has been identified as the fifth-leading cause of mortality among Iranian women [4] . In Iran, 15,492 new cases of breast cancer were diagnosed in 2022, making up 11.3% of all new cancer cases [5] . The exact causes of breast cancer are still unknown, but there are both modifiable and non-modifiable risk factors associated with this malignancy [6]. Some of the main non-modifiable risk factors include aging [7], being a woman [8], having a family history of breast cancer [9], possessing certain genetic mutations [10], late menopause, and early menarche [11] . On the other hand, modifiable risk factors for breast cancer include obesity [8], alcohol, smoking [12], postmenopausal hormone therapy [13], and being single [14] .  \nEarly detection of breast cancer can lead to lower treatment costs and more effective treatment. Thus, knowledge about the main risk factors that affect this mysterious cancer is a crucial task [15] .  \nMachine learning algorithms have a significant impact on the healthcare system by analyzing large data sets, and assisting to make decisions about a pat","cbCaia5vWSRSfAby","https://ap.wps.com/l/cbCaia5vWSRSfAby","pdf",1751623,1,6,"English","en",105,"# Summary\n## Background\n## Objectives\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What is the main objective of the study in Mashhad, Iran?\",\"answer\":\"To apply machine learning techniques to recognize the most important risk factors of breast cancer among women in Mashhad, Iran.\"},{\"question\":\"Which machine learning models were used?\",\"answer\":\"Decision Tree, Random Forest, Logistic Regression, and Principal Component Analysis were applied using RStudio.\"},{\"question\":\"How did the key risk factors differ across models?\",\"answer\":\"Decision Tree and Random Forest emphasized factors such as family cancer and individual breast cancer history, while PCA and Logistic Regression highlighted factors including family cancer, pregnancy-related variables, abortion, and reproductive timing.\"}]","Machine learning techniques to identify risk factors of breast cancer among women in Mashhad, Iran - research summary | PDF",1785814225,15,{"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-techniques-to-identify-risk-factors-of-breast-cancer-among-women-in-mashhad-iran-research-summary","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-techniques-to-identify-risk-factors-of-breast-cancer-among-women-in-mashhad-iran-research-summary/123024/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study in Mashhad, Iran?","Question",{"text":75,"@type":76},"To apply machine learning techniques to recognize the most important risk factors of breast cancer among women in Mashhad, Iran.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were used?",{"text":80,"@type":76},"Decision Tree, Random Forest, Logistic Regression, and Principal Component Analysis were applied using RStudio.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the key risk factors differ across models?",{"text":84,"@type":76},"Decision Tree and Random Forest emphasized factors such as family cancer and individual breast cancer history, while PCA and Logistic Regression highlighted factors including family cancer, pregnancy-related variables, abortion, and reproductive timing.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]