Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction Using Machine Learning Technique

• Published in: Advances in fuzzy systems Vol. 2024; pp. 1 - 11
• Main Authors: Ibrahim, Muhammad, Abbas, Sagheer, Fatima, Areej, Ghazal, Taher M., Saleem, Muhammad, Alharbi, Meshal, Alotaibi, Fahad Mazaed, Adnan Khan, Muhammad, Waqas, Muhammad, Elmitwally, Nouh
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2024; Hindawi Limited
• Subjects: Accuracy; Algorithms; Anemia; Blood diseases; Blood tests; Blood transfusions; Children; Classification; Datasets; Decision trees; Erythrocytes; Fuzzy logic; Health care; Hemoglobin; Iron; Logistics; Lungs; Machine learning; Malaria; Medical research; Neural networks; Patients; Performance evaluation; Polypeptides; Regression analysis; Support vector machines
• ISSN: 1687-7101; 1687-711X
• DOI: 10.1155/2024/4468842

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, Naïve 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.