TY - JOUR
T1 - Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction Using Machine Learning Technique
AU - Ibrahim, Muhammad
AU - Abbas, Sagheer
AU - Fatima, Areej
AU - Ghazal, Taher M.
AU - Saleem, Muhammad
AU - Alharbi, Meshal
AU - Alotaibi, Fahad Mazaed
AU - Adnan Khan, Muhammad
AU - Waqas, Muhammad
AU - Elmitwally, Nouh
N1 - Publisher Copyright:
© 2024 Muhammad Ibrahim et al.
PY - 2024/3/25
Y1 - 2024/3/25
N2 - 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, Naive Bayes, Decision Tree, and Neural Network, they 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.
AB - 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, Naive Bayes, Decision Tree, and Neural Network, they 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.
UR - https://www.open-access.bcu.ac.uk/15362/
U2 - 10.1155/2024/4468842
DO - 10.1155/2024/4468842
M3 - Article
SN - 1687-7101
VL - 2024
JO - Advances in Fuzzy Systems
JF - Advances in Fuzzy Systems
M1 - 4468842
ER -