[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118401-en":3,"doc-seo-118401-105":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},118401,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",7,"Healthcare","Analyzing the Application of Machine Learning in Anemia Prediction","This paper explores how machine learning improves anemia prediction for clinical diagnosis and management. Anemia affects large populations worldwide yet remains frequently underdiagnosed due to traditional approaches that depend on clinical judgment and standard laboratory testing. By learning from complex datasets that combine questionnaire responses, clinical indicators, demographic variables, and laboratory results, machine learning can increase prediction accuracy. The review covers decision trees, random forests, support vector machines, and neural networks, while addressing key adoption barriers and highlighting future work on generalizability and interpretability.","Analyzing the Application of Machine Learning in Anemia Prediction  \nYuxi Li  \nFaculty of innovation Engineering, Macau University of Science and Technology, Chongqing, 402167, China  \nAbstract. This paper explores the applications of machine learning in the prediction of anemia, highlighting its potential to revolutionize clinical diagnosis and management. Anemia, a prevalent condition affecting millions globally, is often underdiagnosed due to traditional diagnostic methods that rely on clinical judgment and standard laboratory tests. Machine learning techniques provide innovative solutions by analyzing complex datasets that incorporate questionnaire, clinical features, demographic information, and laboratory results, thereby enhancing the accuracy of anemia predictions.  \nThis paper examines decision trees, random forests, support vector machines, and neural networks, emphasizing their efficacy in identifying patterns and risk factors associated with anemia. Obstacles such as data quality, featureselection, and model interpretability continue to hinder clinical adoption.  \nThe review identifies future research directions aimed at improving model generalizability and interpretability, ensuring that these technologies can be effectively integrated into healthcare practice. This paper advocates for the systematic adoption of machine learning methodologies in anemia management, positing that such innovations are crucial for advancing public  \nhealth and optimizing resource allocation in clinical settings.  \n1 Introduction  \nAnemia is a widespread public health concern characterized by a deficiency of red blood cells or hemoglobin in the bloodstream. This condition can lead to a variety of symptoms, including fatigue, headaches, and palpitations. In severe cases, anemia can elevate the risk of mortality. According to a 2023 World Health Organization survey, nearly 40% of children aged 6 to 59 months, about 37% of pregnant women, and approximately 30% of women aged 15 to 49 years globally are affected by anemia [1] . Therefore, it is important to diagnose and forecast anemia on time.  \nNevertheless, Traditional diagnostic methods for anemia primarily rely on laboratory tests, such as complete blood counts and blood chemistry analyses. However, these methods often have limitations, as various factors can influence their results and may lead to delaysin treatment.  \nMachine learning allows computers to learn without being explicitly programmed, and also based on historical dataset to make delicate forecast. Recently, it has been widely used  \nCorresponding author: [1230005529@student.must.edu.mo](1230005529@student.must.edu.mo)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nto handle data efficiently and forecast in medical field, especially in early disease prediction and risk assessment. For instance, it is difficult to make model by logic regression. Such asthe familial hypercholesterolemia, an arterial thrombotic disorder, and human immunodeficiency virus. However, other types of machine learning models like neural networks which allow transformations of input features to predict outcomes better. It extremely helps researchers process vast amounts of clinical data and uncover potential disease-related features. This offers new approaches and ideas for the early diagnosis and personalized treatment of anemia. In the research which explores the diagnostic procedures between traditional doctors and machine learning in ishaemic heart disease. They use the naive and the semi-naive Bayes and Assistant-R as the skills. Consequently, researchers indicated that step-by-step calculations for post-test probability could significantly enhance the accuracy of machine learning models. These algorithms have shown a 6% improvement in correctly c","cbCaijm0UWtii5HS","https://ap.wps.com/l/cbCaijm0UWtii5HS","pdf",233950,1,5,"English","en",105,"# Introduction\n# Machine Learning in Anemia Prediction","[{\"question\":\"Why is timely anemia diagnosis and forecasting important?\",\"answer\":\"Anemia is a widespread public health problem that can cause symptoms and, in severe cases, increase mortality risk. Early identification supports better clinical management and resource planning.\"},{\"question\":\"Which machine learning models are discussed for anemia prediction?\",\"answer\":\"The paper reviews decision trees, random forests, support vector machines, and neural networks, focusing on their ability to detect patterns and risk factors related to anemia.\"},{\"question\":\"What challenges hinder clinical adoption of machine learning for anemia?\",\"answer\":\"Data quality, feature selection, and model interpretability remain major obstacles that limit reliable integration into routine healthcare workflows.\"}]","Analyzing the Application of Machine Learning in Anemia Prediction | PDF",1785683445,13,{"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},"analyzing-the-application-of-machine-learning-in-anemia-prediction","",{"@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/analyzing-the-application-of-machine-learning-in-anemia-prediction/118401/",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","2026-08-02",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 timely anemia diagnosis and forecasting important?","Question",{"text":75,"@type":76},"Anemia is a widespread public health problem that can cause symptoms and, in severe cases, increase mortality risk. Early identification supports better clinical management and resource planning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are discussed for anemia prediction?",{"text":80,"@type":76},"The paper reviews decision trees, random forests, support vector machines, and neural networks, focusing on their ability to detect patterns and risk factors related to anemia.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges hinder clinical adoption of machine learning for anemia?",{"text":84,"@type":76},"Data quality, feature selection, and model interpretability remain major obstacles that limit reliable integration into routine healthcare workflows.","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},[93,97,101,105,109,114,117,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"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":21,"slug":137},19,"General","general"]