{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T06:35:54Z","timestamp":1792218954730,"version":"4.4.0"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T00:00:00Z","timestamp":1711324800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Advances in Fuzzy Systems"],"published-print":{"date-parts":[[2024,3,25]]},"abstract":"<jats:p>The abnormality of haemoglobin in the human body is the fundamental cause of thalassemia disease. Thalassemia is considered a common genetic blood condition that has received extensive investigation in medical research globally. Likely, inherited disorders will be passed down to children from their parents. If both parents are beta Thalassemia carriers, 25% of their children will have intermediate or major beta thalassemia, which is fatal. An efficient method of beta thalassemia is prenatal screening after couples have received counselling. Identifying Thalassemia carriers involves a costly, time-consuming, and specialized test using quantifiable blood features. However, cost-effective and speedy screening methods must be developed to address this issue. The demise rate due to thalassemia development is outstandingly high around the globe. The passing rate due to thalassemia development can be reduced by following the proper procedure early; otherwise, it significantly impacts the body. A machine learning-based late fusion model proposes the detection of beta-thalassemia carriers by analyzing red blood cells. This study applied the late fusion technique to employ four machine learning algorithms. For identifying the beta-thalassemia carriers, logistics regression, Na\u00efve Bayes, decision tree, and neural network have achieved an accuracy of 94.01%, 93.15%, 97.93%, and 98.07%, respectively, by using the features-based dataset. The late fusion-based ML model achieved an overall accuracy of 96% for detecting beta-thalassemia carriers. The proposed late fusion model performs better than previously published approaches regarding efficiency, reliability, and precision.<\/jats:p>","DOI":"10.1155\/2024\/4468842","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:20:06Z","timestamp":1711412406000},"page":"1-11","source":"Crossref","is-referenced-by-count":22,"title":["Fuzzy-Based Fusion Model for \u03b2-Thalassemia Carriers Prediction Using Machine Learning Technique"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7692-0917","authenticated-orcid":true,"given":"Muhammad","family":"Ibrahim","sequence":"first","affiliation":[{"name":"School of Computer Science, National College of Business Administration & Economics, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5289-7831","authenticated-orcid":true,"given":"Sagheer","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bahria University Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7264-7941","authenticated-orcid":true,"given":"Areej","family":"Fatima","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Lahore Garrison University, Lahore, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0672-7924","authenticated-orcid":true,"given":"Taher M.","family":"Ghazal","sequence":"additional","affiliation":[{"name":"Centre for Cyber Physical Systems, Computer Science Department, Khalifa University, Abu Dhabi, UAE"},{"name":"Center for Cyber Security, Faculty of Information Science and Technology, UKM, Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5209-8375","authenticated-orcid":true,"given":"Muhammad","family":"Saleem","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minhaj University, Lahore, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6025-7858","authenticated-orcid":true,"given":"Meshal","family":"Alharbi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9885-1775","authenticated-orcid":true,"given":"Fahad Mazaed","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-9935","authenticated-orcid":true,"given":"Muhammad","family":"Adnan Khan","sequence":"additional","affiliation":[{"name":"School of Computing, Skyline University College, University City Sharjah, Sharjah 1797, UAE"},{"name":"Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan"},{"name":"Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3842-0376","authenticated-orcid":true,"given":"Muhammad","family":"Waqas","sequence":"additional","affiliation":[{"name":"School of Computer Science, National College of Business Administration & Economics, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3030-9696","authenticated-orcid":true,"given":"Nouh","family":"Elmitwally","sequence":"additional","affiliation":[{"name":"School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"},{"name":"Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"issue":"1","key":"1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13023-021-01947-6","article-title":"The parental perspective of thalassaemia in Bangladesh: lack of knowledge, regret, and barriers","volume":"16","author":"M. 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