[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127099-en":3,"doc-seo-127099-105":30,"detail-sidebar-cat-0-en-105":92},{"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},127099,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction","The study compares three machine learning methods—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree—for menstrual cycle analysis and prediction. Using a dataset from Kaggle with 1,665 samples and 80 attributes, models are evaluated with accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Results indicate LSTM achieves the highest accuracy (91.3%), followed by CNN (88.9%) and Decision Tree (85.2%). Findings support improved reproductive health tracking and motivate future work incorporating hormonal history, lifestyle factors, and stronger data privacy protections.","Comparison of Machine Learning Methods for Menstrual Cycle Analysis  \nand Prediction  \nMutiara Khairunisa 1*, Desak Made Sidantya Amanda Putri 2*, I Gusti Ngurah Lanang Wijayakusuma 3*  \n* Matematika, Universitas Udayana  \n[mutiarak062@student.unud.ac.id](mutiarak062@student.unud.ac.id1)[1](mutiarak062@student.unud.ac.id1), [desakamandaptr071@student.unud.ac.id](desakamandaptr071@student.unud.ac.id2)[2](desakamandaptr071@student.unud.ac.id2), [lanang_wijaya@unud.ac.id](lanang_wijaya@unud.ac.id3)[3](lanang_wijaya@unud.ac.id3)  \n\n| Article history:\u003Cbr>Received 2025-01-11 Revised 2025-01-20 Accepted 2025-01-30 | This study compares three machine learning methods—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree—for analyzing and predicting menstrual cycles. The dataset consists of 1,665 samples with 80 attributes encompassing information related to menstrual health. These methods were evaluated using accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. The results show that LSTM achieved the highest accuracy (91.3%), followed by CNN (88.9%) and Decision Tree (85.2%). LSTM excelled in capturing complex temporal patterns in menstrual cycle data, while CNN effectively identified key patterns, and Decision Tree offered interpretability despite lower performance. This study concludes that LSTM is the most effective model for menstrual cycle prediction. The findings highlight the potential for improved accuracy in reproductive health tracking, with future research opportunities to incorporate additional variables such as hormonal history and lifestyle factors, as well as a focus on data privacy.\u003Cbr>\u003Cbr>This is an open access article under the CC–BY-SA license. |\n| --- | --- |\n| Keyword:\u003Cbr>CNN,\u003Cbr>Decision Tree,\u003Cbr>LSTM,\u003Cbr>Machine Learning, Menstrual Cycle Prediction. |  |\n\nArticle Info ABSTRACT  \nI. INTRODUCTION  \nThe menstrual cycle is an important aspect of women's reproductive health that is often overlooked or poorly understood. A better understanding of daily menstrual patterns can empower women and healthcare professionals to identify and manage associated conditions such as polycystic ovary syndrome (PCOS) and endometriosis [1]. However, manual tracking of menstrual cycles is often inaccurate due to user compliance factors and the inherent variability of menstrual experiences [2] .  \nIn recent years, the use of machine learning to analyze and predict the menstrual cycle has emerged as a promising approach. A study showed that machine learning can help classify problems in the menstrual cycle that are often overlooked, providing a better understanding of potential irregularities [3]. For instance, the Random Forest algorithm has been applied to analyze menstrual cycle data from over 800 women, achieving an accuracy of up to 89%, even when data was incomplete or inconsistent [4]. Additionally, a study comparing Support Vector Machine (SVM) and Decision Tree algorithms demonstrated that incorporating menstrual  \nvariables significantly improved classification accuracy. SVM achieved 96.91% accuracy, outperforming Decision Tree at 93.81%, highlighting the importance of menstrualrelated features in improving model performance, particularly in addressing stress-related menstrual irregularities [5] .  \nHowever, the use of menstrual cycle tracking apps also raises concerns regarding the security and privacy of user data. A study conducted a security analysis on menstrual cycle tracking apps using static, dynamic, and machine learning techniques found that many apps lack essential security features, potentially exposing users’ personal information [6]. To address these issues, future menstrual tracking applications should implement technical measures such as end-to-end encryption for secure data transmission, anonymization techniques to protect personally identifiable information, and encrypted databases for secure storage. Furthermore, adherence to international ","cbCaif9wcIviQV6P","https://ap.wps.com/l/cbCaif9wcIviQV6P","pdf",414670,1,6,"English","en",105,"# Abstract\n# Introduction\n# Method\n## Data Collection\n## Model Evaluation","[{\"question\":\"Which machine learning models are compared for menstrual cycle prediction?\",\"answer\":\"The study compares LSTM, CNN, and Decision Tree for analyzing and predicting menstrual cycles.\"},{\"question\":\"How is model performance evaluated in this research?\",\"answer\":\"Performance is assessed using accuracy, MAPE, and RMSE metrics.\"},{\"question\":\"What model performs best and how is it interpreted?\",\"answer\":\"LSTM performs best with 91.3% accuracy, attributed to its strength in capturing complex temporal patterns in menstrual cycle data.\"}]","Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction | PDF",1785936838,15,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"comparison-of-machine-learning-methods-for-menstrual-cycle-analysis-and-prediction","",{"@graph":36,"@context":86},[37,54,69],{"@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/comparison-of-machine-learning-methods-for-menstrual-cycle-analysis-and-prediction/127099/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning models are compared for menstrual cycle prediction?","Question",{"text":76,"@type":77},"The study compares LSTM, CNN, and Decision Tree for analyzing and predicting menstrual cycles.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is model performance evaluated in this research?",{"text":81,"@type":77},"Performance is assessed using accuracy, MAPE, and RMSE metrics.",{"name":83,"@type":74,"acceptedAnswer":84},"What model performs best and how is it interpreted?",{"text":85,"@type":77},"LSTM performs best with 91.3% accuracy, attributed to its strength in capturing complex temporal patterns in menstrual cycle data.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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":107,"slug":138},19,"General","general"]