TY - GEN
T1 - Hybrid Model for Prediction of Treatment Response in Beta-thalassemia Patients with Hepatitis C Infection
AU - Hussein, Aisha Mohamed
AU - Sharaf-Eldin, Ahmed
AU - Abdo, Amany
AU - Kamal, Sanaa Moharram
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Patients with beta-thalassaemia major (β-TM) who get regular blood transfusions are at risk of iron overload and hepatitis C virus (HCV) infection. These double injuries together can lead to chronic liver damage. Treatment with pegylated interferon combined ribavirin (Peg-IFN/RBV) is associated with side effects that compromise the patients’ quality of life. The efficacy of two anti-viral regimens (Peg-IFN/RBV) and Peg-IFN monotherapy were assessed using a machine learning model to identify patients who could achieve sustained virologic response (SVR) with HCV eradication. This paper is a follow-up study of our previous published paper that used a different method to address the same research question. A hybrid Neuro-SVM model was developed to improve the accuracy of classification that shows 98.83% in group 1 and 99.75 in group 2 and conveyed as a graphical user interface that can help the clinical support decision in the prediction of optimal treatment response. The model was compared to artificial neural network (ANN), support vector machine (SVM) and naïve Bayesian (NB). Using the hybrid model, it would be useful if we distinguish in advance those patients who may benefit from the approved direct anti-viral agents (DAAs) therapy from those who would not.
AB - Patients with beta-thalassaemia major (β-TM) who get regular blood transfusions are at risk of iron overload and hepatitis C virus (HCV) infection. These double injuries together can lead to chronic liver damage. Treatment with pegylated interferon combined ribavirin (Peg-IFN/RBV) is associated with side effects that compromise the patients’ quality of life. The efficacy of two anti-viral regimens (Peg-IFN/RBV) and Peg-IFN monotherapy were assessed using a machine learning model to identify patients who could achieve sustained virologic response (SVR) with HCV eradication. This paper is a follow-up study of our previous published paper that used a different method to address the same research question. A hybrid Neuro-SVM model was developed to improve the accuracy of classification that shows 98.83% in group 1 and 99.75 in group 2 and conveyed as a graphical user interface that can help the clinical support decision in the prediction of optimal treatment response. The model was compared to artificial neural network (ANN), support vector machine (SVM) and naïve Bayesian (NB). Using the hybrid model, it would be useful if we distinguish in advance those patients who may benefit from the approved direct anti-viral agents (DAAs) therapy from those who would not.
KW - Artificial neural networks
KW - Beta-thalassemia major
KW - Machine learning
KW - Synthetic minority oversampling
UR - https://www.scopus.com/pages/publications/85115191579
U2 - 10.1007/978-981-16-2275-5_37
DO - 10.1007/978-981-16-2275-5_37
M3 - Conference contribution
AN - SCOPUS:85115191579
SN - 9789811622748
T3 - Lecture Notes in Networks and Systems
SP - 561
EP - 584
BT - Digital Transformation Technology - Proceedings of ITAF 2020
A2 - Magdi, Dalia A.
A2 - Helmy, Yehia K.
A2 - Mamdouh, Mohamed
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd World Conference on Internet of Things: Applications and Future, ITAF 2020
Y2 - 16 December 2020 through 17 December 2020
ER -
