[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124371-en":3,"doc-seo-124371-105":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},124371,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",7,"Healthcare","Fetal electrocardiogram prediction using machine learning - a random forest-based approach","Monitoring fetal health during pregnancy supports safe delivery and newborn well-being, and fetal electrocardiography (fetal ECG) is a key tool for cardiac assessment. Interpretation remains difficult because fetal ECG signals are complex and variable, while existing methods suffer from human error and limited automation. This study applies machine learning, especially random forest, to predict and analyze fetal ECGs. Compared with ANN, SVM, and RNN, the proposed approach delivers higher inaccuracy reduction, robustness, and reliability, with methodology, implementation, and results discussed for clinical relevance.","Fetal electrocardiogram prediction using machine learning: a random forest-based approach  \nMohammed Moutaib1, Mohammed Fattah1, Yousef Farhaoui2, Badraddine Aghoutane3,  \nMoulhime El Bekkali4  \n1IMAGE Laboratory, University of Moulay Ismail, Meknes, Morocco 2L-STI, T-IDMS, Faculty of Science and Technics, University of Moulay Ismail, Errachidia, Morocco 3Faculty of Science, University of Moulay Ismail, Meknes, Morocco  \n4IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco  \nArticle history:  \nReceived Sep 15, 2023 Revised Nov 17, 2023 Accepted Nov 30, 2023  \nKeywords:  \nArtificial neural network Electrocardiogram Fetal electrocardiograms K-means  \nMachine learning Recurrent neural network Support vector machines  \nCorresponding Author:  \nMonitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods inaccuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forestbased solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.  \nThis is an open access article under the CC BY-SA license.  \nMohammed Moutaib  \nIMAGE Laboratory, Moulay Ismail University Meknes, Morocco [Email: mohammed.93@live.fr](Email: mohammed.93@live.fr)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nFetal health is a crucial indicator of an unborn fetus ’s overall well-being and development. A significant aspect of this monitoring is assessing fetal heart health, often performed via the fetal electrocardiogram (fetal ECG) [1] . These cardiac data can be indicative of a variety of medical conditions, including congenital disabilities and warning signs of fetal distress. Although the fetal ECG is a powerful diagnostic tool, its interpretation often requires significant expertise and is subject to human error [2] . In a world where healthcare systems are under increasing pressure, reliable and automated analysis of this data is not only desirable but almost essential [3] .  \nDespite the importance of fetal ECG signals for early diagnosis and prevention of potential complications, current analysis methods face several limitations, such as the risk of human error, lack of automation, and sensitivity to signal disturbances. However, interpreting this data can be complex with the vast data set that an ECG produces and the subtle nuances it can contain. Hence the importance of exploiting  \ntechnological advances [4], particularly in the field of machine learning, to improve and refine these interpretations. With their ability to process and analyze large amounts of data, machine learning methods offer remarkable potential to revolutionize the way we approach fetal ECG.  \nPrevious approaches to fetal ECG analysis include rule-based methods, statistical algorithms, and heuristic models [5] . However, these methods often lack precision and do not adapt well to variations and anomalies in the data. The application of machine learning in this area is stil","cbCailIQfR31CVlA","https://ap.wps.com/l/cbCailIQfR31CVlA","pdf",644647,1,8,"English","en",105,"# Introduction\n## Objective and significance\n## Article organization\n# Literature review\n## Fetal monitoring and limitations","[{\"question\":\"Why is fetal ECG prediction important during pregnancy?\",\"answer\":\"Fetal ECG monitoring helps assess fetal cardiac health and detect conditions that may indicate congenital disabilities or fetal distress, supporting safer delivery and better newborn outcomes.\"},{\"question\":\"What machine learning approach is proposed in the study?\",\"answer\":\"The study proposes an automated fetal ECG prediction method using a random forest algorithm, aiming for accurate and robust predictions across different signal qualities and clinical conditions.\"},{\"question\":\"How does the random forest approach compare with other methods?\",\"answer\":\"The random forest-based approach outperforms other standard techniques such as ANN, SVM, and RNN in terms of inaccuracy reduction, robustness, and reliability.\"}]","Fetal electrocardiogram prediction using machine learning - a random forest-based approach | PDF",1785821859,20,{"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},"fetal-electrocardiogram-prediction-using-machine-learning-a-random-forest-based-approach","",{"@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/fetal-electrocardiogram-prediction-using-machine-learning-a-random-forest-based-approach/124371/",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},"Why is fetal ECG prediction important during pregnancy?","Question",{"text":75,"@type":76},"Fetal ECG monitoring helps assess fetal cardiac health and detect conditions that may indicate congenital disabilities or fetal distress, supporting safer delivery and better newborn outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approach is proposed in the study?",{"text":80,"@type":76},"The study proposes an automated fetal ECG prediction method using a random forest algorithm, aiming for accurate and robust predictions across different signal qualities and clinical conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the random forest approach compare with other methods?",{"text":84,"@type":76},"The random forest-based approach outperforms other standard techniques such as ANN, SVM, and RNN in terms of inaccuracy reduction, robustness, and reliability.","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},[93,97,101,105,110,115,118,122,126,129,133],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":29,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":29,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]