A Systematic Review on Thalassemia Using Neural Network Techniques
Gurbinder Kaur, Vijay Kumar Garg · 2024
The genetic blood disorder thalassemia is brought on by a lack of haemoglobin synthesis, the primary protein found in RBCs. Its function is to distribute oxygen to all of the body’s organs from the lungs. Haemoglobin is composed of four chains of globin, which are two β-chains and two α-chains. Clinicians perform a CBC test as well as a haemoglobin test to identify thalassemia. Machine learning has been the subject of extensive early study. Several machine learning methods, including K-nearest neighbour, decision trees, support vector machines, and naive Bayes, perform better in their respective fields. Currently, one of the hottest research areas in medicine is neural networks. In this research, different neural network methods for thalassemia diagnosis are reviewed, including artificial neural networks, convolutional neural networks, radial basis functions, multilayer perceptrons, etc. These algorithms have better accuracy, sensitivity, and specificity as compared to machine learning. This survey in medical diagnostics is primarily intended to serve as a roadmap for researchers as they create the most practical, user-friendly, and affordable technologies, methods, and clinical approaches.