[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123851-en":3,"doc-seo-123851-105":30,"detail-sidebar-cat-0-en-105":95},{"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},123851,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",7,"Healthcare","Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction - Using Machine Learning Technique","The abnormality of haemoglobin is the primary cause of thalassemia, a common inherited genetic blood disorder studied globally. When both parents are β-thalassemia carriers, 25% of offspring may develop intermediate or major disease with fatal risk. Prenatal screening is effective but requires costly, time-consuming, specialized blood tests. A late-fusion machine learning approach is proposed using red blood cell features and four algorithms, achieving strong carrier detection accuracy and outperforming prior work in efficiency and reliability.","1 Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction  \n2 Using Machine Learning Technique  \n3 Muhammad Ibrahim 1, Sagheer Abbas,2 Areej Fatima,3 Taher M. Ghazal,4,5 Muhammad Saleem,6  \n4 Meshal Alharbi,7 Fahad Mazaed Alotaibi,8 Muhammad Adnan Khan,9,10 Muhammad Waqas 1  \n5 and Nouh Elmitwally11,12  \n6 1 School of Computer Science, National College of Business Administration & Economics, 7 Lahore, 54000, Pakistan.  \n8 2Department of Computer Science, Bahria University Lahore Campus, Lahore, 54000, 9 Pakistan  \n10 3Department of Computer Science, Lahore Garrison University, Lahore, Pakistan  \n11 4Centre for Cyber Physical Systems, Computer Science Department, Khalifa University  \n12 5Center for Cyber Security, Faculty of Information Science and Technology, UKM, 43600  \n13 Bangi, Selangor, Malaysia  \n14 6 School of Computer Science, Minhaj University Lahore, Pakistan  \n15 7Department of Computer Science, College of Computer Engineering and Sciences, Prince  \n16 Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia.  \n17 8Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz  \n18 University, Jeddah, Saudi Arabia  \n19 9 School of Information Technology, Skyline University College, University City Sharjah, 20 Sharjah, 1797, UAE  \n21 10Riphah School of Computing and Innovation, Faculty of Computing, Riphah International  \n22 University, Lahore Campus, Lahore 54000, Pakistan  \n23 11School of Computing and Digital Technology, Birmingham City University,  \n24 Birmingham B4 7XG, UK  \n25 12Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo  \n26 University, Giza 12613, Egypt  \n27 Correspondence should be addressed to Sagheer Abbas;[jamsagheer@gmail.com](jamsagheer@gmail.com)  \n28 Abstract  \n29 The abnormality of haemoglobin in the human body is the fundamental cause of thalassemia  \n30 disease. Thalassemia is considered a common genetic blood condition that has received  \n31 extensive investigation in medical research globally. Likely, inherited disorders will be passed  \n32 down to children from their parents. If both parents are beta Thalassemia carriers, 25% of their  \n33 children will have intermediate or major beta thalassemia, which is fatal. An efficient method  \n34 of beta thalassemia is prenatal screening after couples have received counselling. Identifying  \n35 Thalassemia carriers involves a costly, time-consuming, and specialized test using quantifiable  \n36 blood features. However, cost-effective and speedy screening methods must be developed to  \n37 address this issue. The demise rate due to thalassemia development is outstandingly high  \n38 around the globe. The passing rate due to thalassemia development can be reduced by  \n39 following the proper procedure early; otherwise, it significantly impacts the body. A machine  \n40 learning-based late fusion model proposes the detection of beta-thalassemia carriers by  \n41 analyzing red blood cells. This study applied the late fusion technique to employ four machine  \n42 learning algorithms. For identifying the beta thalassemia carriers, Logistics Regression, Naïve  \n43 Bayes, Decision Tree, and Neural Network, they have achieved an accuracy of 94.01%,  \n44 93.15%, 97.93%, and 98.07%, respectively, by using the features-based dataset. The late 45 fusion-based ML model achieved an overall accuracy of 96% for detecting beta-thalassemia 46 carriers. The proposed late fusion model performs better than previously published approaches 47 regarding efficiency, reliability, and precision.  \n48 Keywords: Machine Learning (ML), Logistics Regression (LR), Naïve Bayes (NB), Decision Tree  \n49 (DT), Neural Network (NN), Fuzzy Logic (FL), Internet of medical things (IoMT), Late Fusion  \n50 model.  \n51 Introduction  \n52 Thalassemia comes from the Greek terms 'Thalassa' and 'Haima.' \"Thalassa\" means \"the ocean,\" and  \n53 \"Haima\" means \"the blood\" . Thalassemia is a genetic blood disorder characterized by insufficie","cbCaiiSJDdkFmx4G","https://ap.wps.com/l/cbCaiiSJDdkFmx4G","pdf",659414,1,15,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Why is early detection of β-thalassemia carriers important?\",\"answer\":\"Early detection helps reduce death rates associated with thalassemia progression by supporting timely and proper screening decisions.\"},{\"question\":\"What screening challenge does the study address?\",\"answer\":\"Traditional carrier identification relies on costly, time-consuming, specialized tests using quantifiable blood features, motivating the need for faster and more cost-effective methods.\"},{\"question\":\"How does the proposed model predict β-thalassemia carriers?\",\"answer\":\"The study applies a late fusion machine learning technique to red blood cell feature data, combining four algorithms (Logistic Regression, Naïve Bayes, Decision Tree, and Neural Network).\"},{\"question\":\"What performance was reported for the late fusion approach?\",\"answer\":\"Individual models achieved accuracies of 94.01%, 93.15%, 97.93%, and 98.07%, while the late fusion model reached an overall accuracy of 96% for carrier detection.\"}]","Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction - Using Machine Learning Technique | PDF",1785818881,38,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"fuzzy-based-fusion-model-for-thalassemia-carriers-prediction-using-machine-learning-technique","",{"@graph":36,"@context":89},[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/fuzzy-based-fusion-model-for-thalassemia-carriers-prediction-using-machine-learning-technique/123851/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early detection of β-thalassemia carriers important?","Question",{"text":75,"@type":76},"Early detection helps reduce death rates associated with thalassemia progression by supporting timely and proper screening decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What screening challenge does the study address?",{"text":80,"@type":76},"Traditional carrier identification relies on costly, time-consuming, specialized tests using quantifiable blood features, motivating the need for faster and more cost-effective methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed model predict β-thalassemia carriers?",{"text":84,"@type":76},"The study applies a late fusion machine learning technique to red blood cell feature data, combining four algorithms (Logistic Regression, Naïve Bayes, Decision Tree, and Neural Network).",{"name":86,"@type":73,"acceptedAnswer":87},"What performance was reported for the late fusion approach?",{"text":88,"@type":76},"Individual models achieved accuracies of 94.01%, 93.15%, 97.93%, and 98.07%, while the late fusion model reached an overall accuracy of 96% for carrier detection.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,122,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},40,"healthcare",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},8,"Research & Report",30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]