[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121425-en":3,"doc-seo-121425-105":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},121425,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Advancements in Blood Group Classification - A Novel Approach Using Machine Learning and RF Sensing Technology","Blood group classification is vital for safe blood transfusions, reducing transfusion-related complications, and supporting emergency medical workflows and organ transplantation. A reagent-free RF sensing method at 1.2 GHz extracts distinctive electromagnetic signatures from blood samples using an SDR platform with OFDM subcarriers. Gradient Boosting and Random Forest models process captured wireless channel variations. Testing on 5,840 samples across eight blood groups achieves 97.8% accuracy within 1.5 seconds, faster than conventional laboratory procedures. The integration improves portability, contact-free operation, and suitability for emergency and resource-limited settings, enabling faster point-of-care diagnostics.","This article has been accepted for publication in IEEE Journal of Selected Areas in Sensors. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/JSAS.2025.3601060  \nIEEE JOURNAL OF SELECTED AREAS IN SENSORS 1  \nAdvancements in Blood Group Classification: A Novel Approach Using Machine Learning and  \nRF Sensing Technology  \nMalik Muhammad Arslan, Lei Guan, Xiaodong Yang, Nan Zhao, Abbas Ali Shah, Muhammad Bilal Khan,  \nMubashir Rehman, Syed Aziz Shah, Qammer H. Abbasi  \nAbstract—Blood group classification is critical for enhancing the safety of blood transfusions, preventing transfusion-related complications, and facilitating emergency medical interventions and organ transplantation. Unlike traditional methods that require blood draws and chemical reagents, our approach analyzes the unique electromagnetic signatures of blood samples through radio frequency (RF) sensing at 1.2 GHz. We developed a custom SDR platform that captures subtle variations in orthogonal frequency division multiplexing (OFDM) subcarriers, which are then processed by advanced machine learning algorithms including Gradient Boosting and Random Forest. Testing on 5,840 samples across eight blood groups demonstrated remarkable 97.8% classification accuracy with results delivered in just 1.5 seconds-significantly faster than conventional 30-60 minute laboratory methods. The system's innovative integration of RF sensing and machine learning eliminates the need for reagents or physical contact while maintaining high precision, offering particular advantages for emergency situations and resource-limited settings. This work represents a paradigm shift in blood typing technology, combining the portability ofSDR hardware with the analytical power of machine learning to create a faster, safer alternative to traditional approaches. The demonstrated accuracy and speed suggest strong potential for clinical adoption in transfusion medicine and point-of-care diagnostics.  \nIndex Terms— Machine Learning (ML), Reagent-Free Blood Group Classification, Orthogonal frequency division multiplex (OFDM), Radio frequency (RF), Software-defined radio (SDR), Wireless channel state information (WCSI).  \nINTRODUCTION  \nBlood group determination is essential in medical diagnostics, significantly impacting clinical medicine, transfusion services, and emergency care [1,2] . The early 20thcentury discovery of blood groups transformed transfusions into life-saving procedures [3-5]. Blood group testing is vital in hematology and genetics, where accuracy and timeliness affect therapeutic decisions and patient outcomes [6] . Traditional methods such as serological testing have limitations, including  \nMalik Muhammad Arslan, Lei Guan, Xiaodong Yang, Nan Zhao, Abbas Ali Shah are with the School of Electronic Engineering, Xidian University, Xi’an, Shaanxi, 710071, China.  \nMuhammad Bilal Khan and Mubashir Rehman are with the Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, 43600, Pakistan.  \nsensitivity to antigen variability, interference from autoantibodies, and inefficacy in degraded samples [7] . Advanced methods, such as molecular testing, gel agglutination, microplate agglutination, flow cytometry, and solid-phase red cell adherence assay (SPRCA), offer higher precision, but are costly, complex, and time-consuming [8] . These issues are particularly problematic in emergency situations, where delays are critical [9,10] . More versatile, efficient, and accessible blood group testing methods are urgently needed, particularly in remote or resource-limited regions [11] . Traditional methods also struggle with invasive contact, reliance on specialized reagents, labor-intensive processes, and lengthy processing times, making them less practical in isolated or resource-limited settings. Their accuracy can also be compromised in complex cases, such as those inv","cbCailJ1Rma3PPxB","https://ap.wps.com/l/cbCailJ1Rma3PPxB","pdf",2839434,1,12,"English","en",105,"# Abstract\n# Introduction\n## Background and limitations of traditional blood typing\n## RF sensing and SDR for blood group classification","[{\"question\":\"How does the proposed method classify blood groups without reagents or physical contact?\",\"answer\":\"It analyzes the unique electromagnetic signatures of blood samples using radio frequency sensing at 1.2 GHz with a custom SDR platform, then processes captured OFDM subcarriers using machine learning models.\"},{\"question\":\"Which machine learning algorithms are used in the blood group classification pipeline?\",\"answer\":\"The study uses Gradient Boosting and Random Forest to learn patterns from the processed RF/OFDM-derived features.\"},{\"question\":\"What performance was achieved on the dataset, and how fast is the system?\",\"answer\":\"On 5,840 samples across eight blood groups, the method reports 97.8% classification accuracy with results delivered in about 1.5 seconds, compared with 30–60 minutes in conventional laboratory methods.\"}]","Advancements in Blood Group Classification - A Novel Approach Using Machine Learning and RF Sensing Technology | PDF",1785735611,30,{"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},"advancements-in-blood-group-classification-a-novel-approach-using-machine-learning-and-rf-sensing-technology","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/advancements-in-blood-group-classification-a-novel-approach-using-machine-learning-and-rf-sensing-technology/121425/",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","2026-08-03",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},"How does the proposed method classify blood groups without reagents or physical contact?","Question",{"text":75,"@type":76},"It analyzes the unique electromagnetic signatures of blood samples using radio frequency sensing at 1.2 GHz with a custom SDR platform, then processes captured OFDM subcarriers using machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used in the blood group classification pipeline?",{"text":80,"@type":76},"The study uses Gradient Boosting and Random Forest to learn patterns from the processed RF/OFDM-derived features.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance was achieved on the dataset, and how fast is the system?",{"text":84,"@type":76},"On 5,840 samples across eight blood groups, the method reports 97.8% classification accuracy with results delivered in about 1.5 seconds, compared with 30–60 minutes in conventional laboratory methods.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"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":29,"slug":121},"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"]