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A cross-sectional dataset of 972 women with abnormal uterine bleeding (Jan 2016–Jan 2021) links patient essential characteristics and curettage pathology results. Models including artificial neural networks, classification and regression trees, support vector machine, and logistic regression are compared to classify cancerous versus non-cancerous cases. Logistic regression shows the best predictive performance.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/endometrial-cancer-in-women-with-abnormal-uterine-bleeding-data-mining-classification-methods/386104/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/endometrial-cancer-in-women-with-abnormal-uterine-bleeding-data-mining-classification-methods/386104.png","ImageObject",300,407,{"name":92,"@type":93},"Riley West","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why focus on early diagnosis and screening for endometrial cancer?","Question",{"text":112,"@type":113},"Early diagnosis enables treatment and can add value by identifying patients through a non-invasive screening approach, especially among high-risk groups.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and methods were used to build the prediction models?",{"text":117,"@type":113},"The study analyzed 972 patients’ essential characteristics and curettage pathology findings, using machine learning algorithms including artificial neural networks, classification and regression trees, support vector machine, and logistic regression.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best for predicting endometrial cancer?",{"text":121,"@type":113},"Logistic regression achieved the best performance, with reported sensitivity and specificity of 100% and 98% (sensitivity) and 98.83% and 98.7% (specificity) for trained and test datasets, respectively.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},386104,1790726828,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46},1099523885074,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Original Article  \nFarah Farzaneh (MD) 1 Azadeh Jafari Ashtiani (MD) 1* Mohammad Hashemi (PhD) 2 Maryam Sadat Hosseini (MD) 1 Maliheh Arab (MD) 1 Tahereh Ashrafganjoei (MD) 1  \nShaghayegh Hooshmand Chayjan (MD) 1  \n1. Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran  \n2. Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran  \n* Correspondence: Azadeh Jafari Ashtiani,  \nPreventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran  \nE-mail: [azade.jafari@gmail.com](azade.jafari@gmail.com)  \n[Tel:](Tel:) +98 2173432437  \nReceived: 16 June 2022  \nRevised: 16 July 2022  \nAccepted: 14 Aug 2022  \nCaspian Journal of Internal Medicine 2023 (Summer); 14(3): 526-533  \nDOI: 10.22088/cjim.14.3.526  \nEndometrial cancer in women with abnormal uterine bleeding: Data mining classification methods  \nAbstract  \nBackground: Over the last decade, artificial intelligence in medicine has been growing. Since endometrial cancer can be treated with early diagnosis, finding a non-invasive method for screening patients, especially high-risk ones, could have a particular value. Regarding the importance of this issue, we aimed to investigate the risk factors related to endometrial cancer and find a tool to predict it using machine learning.  \nMethods: In this cross-sectional study, 972 patients with abnormal uterine bleeding from January 2016 to January 2021 were studied, and the essential characteristics of each patient, along with the findings of curettage pathology, were analyzed using statistical methods and machine learning algorithms, including artificial neural networks, classification and regression trees, support vector machine, and logistic regression.  \nResults: Out of 972 patients with a mean age of 45.77 ± 10.70 years, 920 patients had benign pathology, and 52 patients had endometrial cancer. In terms of endometrial cancer prediction, the logistic regression model had the best performance (sensitivity of 100% and 98%, specificity of 98.83% and 98.7%, for trained and test data sets respectively,) followed by the classification and regression trees model.  \nConclusion: Based on the results, artificial intelligence-based algorithms can be applied as a non-invasive screening method for predicting endometrial cancer.  \nKeywords: Endometrial cancer, Artificial intelligence, Machine learning.  \nCitation:  \nFarzaneh F, Jafari Ashtiani A, Hashemi M, et al. Endometrial cancer in women with abnormal uterine bleeding: Data mining classification methods. Caspian J Intern Med 2023; 14(3): 526-533.  \nOne of the most common cancers among women is endometrial cancer, which is the fourth most common cancer in the United States after breast, lung and colorectal cancers (1) . Various causes can be considered risk factors for endometrial cancer, high levels of estrogen, high blood pressure, premature menstruation, late menopause, tamoxifen use, nulliparity, Lynch syndrome and old age are risk factors (55≤) (1-6) . According to the American Cancer Society, all women over the age of 65 should be aware of the risk factors and symptoms of endometrial cancer so that they can be referred for further evaluation if any symptoms occur. Abnormal uterine bleeding is one of the most common manifestations of endometrial cancer, especially after menopause, which should be evaluated (1) . Endometrial cancer is on the rise due to increased life expectancy and the prevalence of obesity (2) . Evidence suggests that endometrial adenocarcinoma is more likely to be treated than other female cancers; observing the early signs of abnormal vaginal bleeding causes patients to see a specialist more quickly and receive treatment in the early stages ofthe disease (7, 8) . Although abnormal uterine bleeding is the most common symptom, it is not a good indicator of endometrial cancer. Only 10% of women with cancer present with this symptom, and 90% of women undergoing a","cbCaiqtLAZ95ZLCE","https://ap.wps.com/l/cbCaiqtLAZ95ZLCE","pdf",443906,"English","# Abstract\n# Background\n# Methods\n# Results\n# Conclusion\n# Keywords\n# Introduction\n# Risk factors and clinical significance\n# Machine learning for screening","[{\"question\":\"Why focus on early diagnosis and screening for endometrial cancer?\",\"answer\":\"Early diagnosis enables treatment and can add value by identifying patients through a non-invasive screening approach, especially among high-risk groups.\"},{\"question\":\"What data and methods were used to build the prediction models?\",\"answer\":\"The study analyzed 972 patients’ essential characteristics and curettage pathology findings, using machine learning algorithms including artificial neural networks, classification and regression trees, support vector machine, and logistic regression.\"},{\"question\":\"Which model performed best for predicting endometrial cancer?\",\"answer\":\"Logistic regression achieved the best performance, with reported sensitivity and specificity of 100% and 98% (sensitivity) and 98.83% and 98.7% (specificity) for trained and test datasets, respectively.\"}]","Endometrial cancer in women with abnormal uterine bleeding: Data mining classification methods | PDF",1790272273]