en Obstetrics & Gynicology Obstetrics & Gynicology Original Article Original Article <span style="font-size:14px;"><span style="font-family:Times New Roman;"><span style="line-height:14pt"><b><i><span style="color:blue">Background</span></i></b><i><span style="color:blue">:</span></i> 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.</span><br> <span style="line-height:14pt"><b><i><span style="color:blue">Methods:</span></i></b> 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.<span style="font-size:10.0pt"></span></span><br> <span style="line-height:14pt"><b><i><span style="color:blue">Results:</span></i></b> Out of 972 patients with a mean age of 45.77 &plusmn; 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.<span style="font-size:10.0pt"></span></span><br> <span style="line-height:14pt"><b><i><span style="color:blue">Conclusion</span></i></b><i><span style="color:blue">:</span></i> Based on the results, artificial intelligence-based algorithms can be applied as a non-invasive screening method for predicting endometrial cancer.<span style="font-size:10.0pt"></span></span></span></span><br> &nbsp; Endometrial Cancer, Artificial Intelligence, Machine Learning 526 533 http://caspjim.com/browse.php?a_code=A-10-2767-1&slc_lang=en&sid=1 Farah Farzaneh F_farzaneh@sbmu.ac.ir 100319475328460043056 100319475328460043056 No Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Azadeh Jafari Ashtiani azade.jafari@gmail.com 100319475328460043057 100319475328460043057 Yes Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Mohammad Mohammad Hashemi mohamm.hashemi@mail.sbu.ac.ir 100319475328460043058 100319475328460043058 No Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran Maryam Sadat Hosseini hoseiny339@yahoo.com 100319475328460043059 100319475328460043059 No Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Maliheh Arab drmarab@sbmu.ac.ir 100319475328460043060 100319475328460043060 No Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Tahereh Ashrafganjoei dr_tganjoei@sbmu.ac.ir 100319475328460043061 100319475328460043061 No Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Shaghayegh Hooshmand Chayjan 100319475328460043062 100319475328460043062 No Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran