Rustam, Furqan and Ashraf, Imran and Jabbar, Shehbaz and Tutusaus, Kilian and Mazas Pérez-Oleaga, Cristina and Pascual Barrera, Alina Eugenia and de la Torre Diez, Isabel UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, kilian.tutusaus@uneatlantico.es, cristina.mazas@uneatlantico.es, alina.pascual@unini.edu.mx, UNSPECIFIED (2022) Prediction β-Thalassemia carriers using complete blood count features. Scientific Reports, 12 (1). ISSN 2045-2322
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Abstract
β-Thalassemia is one of the dangerous causes of the high mortality rate in the Mediterranean countries. Substantial resources are required to save a β-Thalassemia carriers’ life and early detection of thalassemia patients can help appropriate treatment to increase the carrier’s life expectancy. Being a genetic disease, it can not be prevented however the analysis of several indicators in parents’ blood can be used to detect disorders causing Thalassemia. Laboratory tests for Thalassemia are time-consuming and expensive like high-performance liquid chromatography, Complete Blood Count (CBC) with peripheral smear, genetic test, etc. Red blood indices from CBC can be used with machine learning models for the same task. Despite the available approaches for Thalassemia carriers from CBC data, gaps exist between the desired and achieved accuracy. Moreover, the data imbalance problem is studied well which makes the models less generalizable. This study proposes a highly accurate approach for β-Thalassemia detection using red blood indices from CBC augmented by supervised machine learning. In view of the fact that all the features do not carry predictive information regarding the target variable, this study employs a unified framework of two features selection techniques including Principal Component Analysis (PCA) and Singular Vector Decomposition (SVD). The data imbalance between β-Thalassemia carrier and non-carriers is handled by Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN). Extensive experiments are performed using many state-of-the-art machine learning models and deep learning models. Experimental results indicate the superiority of the proposed approach over existing approaches with an accuracy score of 0.96.
| Item Type: | Article |
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| Uncontrolled Keywords: | Computational biology andbioinformatics; Health care |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 05 Dec 2022 10:18 |
| Last Modified: | 17 Jul 2023 07:56 |
| URI: | http://repositorio.funiber.org/id/eprint/4905 |
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