The Universidad Internacional Iberoamericana (International Iberoamerican University) (UNIB) researcher, Dr. Alina Eugenia Pascual Barrera, together with other researchers, proposes an accurate approach for thalassemia detection using CBC red blood cell indices augmented by supervised machine learning.
Thalassemia is one of the leading causes of death in Mediterranean countries. It is an inherited genetic disorder produced as a result of mutations in the deoxyribonucleic acid (DNA) of cells, prompted by the insufficient production of hemoglobin (Hb) in the body. Hemoglobin enables red blood cells to carry oxygen. Hemoglobin deficiency minimizes the survival of red blood cells; this limits the oxygen supply in the body, which is life-threatening.
It is a genetic disease, therefore, it is not possible to prevent it. However, by analyzing various indicators in the blood of individuals, it is possible to detect disorders that cause thalassemia. Currently, significant resources are needed to save the lives of thalassemia carriers, and early detection can increase the life expectancy of carriers.
For this reason, several approaches have been proposed to detect thalassemia carriers, but they are limited by the use of unbalanced data sets, lower classification accuracy, and lower generalization of the models. Considering the above, this study proposes an accurate approach to detect thalassemia using CBC red blood cell indices augmented by supervised machine learning.
Two feature selection techniques have been considered which are Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).
In addition, several experiments have been performed using different machine learning models and state-of-the-art deep learning models. The results have demonstrated the superiority of the proposed approach over existing approaches with an accuracy score of 0.96.
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