[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122413-en":3,"doc-seo-122413-105":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},122413,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",7,"Healthcare","Model for Endometriosis Detection using Machine Learning Algorithms","Endometriosis is a chronic, estrogen-dependent condition marked by endometrial tissue outside the uterine cavity, commonly causing pelvic pain and dysmenorrhea that can severely reduce patients’ physical, mental, and social well-being. This study builds a predictive classification model using four machine learning algorithms—Random Forest, LASSO, SVM, and Naive Bayes. A 1,000-case dataset from the Global Health Data Exchange is cleaned and preprocessed, and each model is evaluated with precision, recall, F1-score, and accuracy. Random Forest achieves the best performance, with 0.99 precision for the endometriosis class and 0.98 overall accuracy.","Document downloaded from the institutional repository of the University of Alcala: [http://ebuah.uah.es/dspace/](http://ebuah.uah.es/dspace/)  \nThis is a posprint version of the following published document:  \nBautista, A., Tardillo, J., Castillo Sequera, J. L. & Wong, L. 2025,“Model for endometriosis detection using machine learning algorthms”, in 2025 7th International Conference on Software Engineering and Computer Science (CSECS) .  \nAvailable at [https://dx.doi.org/10.1109/CSECS64665.2025.11009460](https://dx.doi.org/10.1109/CSECS64665.2025.11009460)  \n© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.  \n(Article begins on next page)  \nModel for Endometriosis Detection using Machine  \nLearning Algorithms  \nAlexis Bautista Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima, Peru  \n[u20181a323@upc.edu.pe](u20181a323@upc.edu.pe)  \nJahir Tardillo Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima, Peru  \n[u201713616@upc.edu.pe](u201713616@upc.edu.pe)  \nJosé Luis Castillo-Sequera Department of Computer Science Universidad de Alcalá Alcalá de Henares, Spain  \n[jluis.castillo@uah.es](jluis.castillo@uah.es)  \nLenis Wong  \nInformation Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima, Peru [pcsilewo@upc.edu.pe](pcsilewo@upc.edu.pe)  \nAbstract—Endometriosis is a chronic disease that affects a considerable percentage of women of reproductive age and is characterized by the presence of endometrial tissue outside the uterine cavity, leading to symptoms such as pelvic pain and dysmenorrhea. The aim of this study is to develop a predictive model for the classification of endometriosis using four Machine Learning algorithms: Random Forest, LASSO, SVM, and Naive Bayes. For this purpose, a dataset from the Global Health Data Exchange was utilized, consisting of 1,000 cases of patients with endometriosis. The methodology included data cleaning and preprocessing, as well as the evaluation of each algorithm's performance using four metrics: precision, recall, F1-Score, and accuracy. The findings revealed that the Random Forest algorithm was the most effective in identifying endometriosis, outperforming the other algorithms with a precision of 0.99 for the \"endometriosis\" class and an overall accuracy of 0.98.  \nKeywords—endometriosis, machine learning, Random Forest, LASSO  \nI. INTRODUCTION  \nEndometriosis, as defined in [16], is the presence of functional endometrial tissue (glands and stroma) outside the uterine cavity. Additionally, it is a chronic, periodically symptomatic, estrogen-dependent disease that affects between 10% and 30% of women of reproductive age and older. According to [13], the primary symptom of endometriosis is pelvic pain, which may occur during vaginal bleeding (dysmenorrhea), during sexual intercourse (dyspareunia), or independently of vaginal bleeding (non-menstrual pelvic pain) . Patients may also experience lower back pain or abdominal discomfort. These symptoms can significantly impact a patient’s physical, mental, and social well-being, thereby impairing their quality of life. Endoscopic Submucosal Dissection (ESD) and Comprehensive Sexuality Education (CSE) provide clinically relevant evaluations of endometriosis symptoms and the disease's impact on patients'lives.  \nContemporary medicine stands at a critical intersection between the growing volume of available medical data and the need for more precise and personalized tools for disease diagnosis, treatment, and management. In this context, Machine Learning (ML) has emerged as a powerful tool with the potential to radically transform medical practice. According to [19]","cbCais0N96ap0Vup","https://ap.wps.com/l/cbCais0N96ap0Vup","pdf",1218569,1,6,"English","en",105,"# Introduction\n# Related Works","[{\"question\":\"What is the goal of this study on endometriosis?\",\"answer\":\"To develop a predictive machine learning model that classifies endometriosis based on patient data.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"Random Forest, LASSO, SVM, and Naive Bayes.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Using precision, recall, F1-score, and accuracy across the endometriosis classification task.\"}]","Model for Endometriosis Detection using Machine Learning Algorithms | PDF",1785810499,15,{"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},"model-for-endometriosis-detection-using-machine-learning-algorithms","",{"@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/model-for-endometriosis-detection-using-machine-learning-algorithms/122413/",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 goal of this study on endometriosis?","Question",{"text":75,"@type":76},"To develop a predictive machine learning model that classifies endometriosis based on patient data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared in the study?",{"text":80,"@type":76},"Random Forest, LASSO, SVM, and Naive Bayes.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated?",{"text":84,"@type":76},"Using precision, recall, F1-score, and accuracy across the endometriosis classification task.","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},[93,97,101,105,110,114,117,122,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",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"]