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Ensemble Model for the Risk of Anemia in Pediatric Patients With Sickle Cell Disorder

Ensemble Model for the Risk of Anemia in Pediatric Patients With Sickle Cell Disorder

Jeremiah Ademola Balogun, Adanze O. Asinobi, Olawale Olaniyi, Samuel Ademola Adegoke, Florence Alaba Oladeji, Peter Adebayo Idowu
Copyright: © 2019 |Volume: 4 |Issue: 2 |Pages: 27
ISSN: 2470-8526|EISSN: 2470-8534|EISBN13: 9781522568773|DOI: 10.4018/IJCCP.2019070103
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MLA

Balogun, Jeremiah Ademola, et al. "Ensemble Model for the Risk of Anemia in Pediatric Patients With Sickle Cell Disorder." IJCCP vol.4, no.2 2019: pp.33-59. http://doi.org/10.4018/IJCCP.2019070103

APA

Balogun, J. A., Asinobi, A. O., Olaniyi, O., Adegoke, S. A., Oladeji, F. A., & Idowu, P. A. (2019). Ensemble Model for the Risk of Anemia in Pediatric Patients With Sickle Cell Disorder. International Journal of Computers in Clinical Practice (IJCCP), 4(2), 33-59. http://doi.org/10.4018/IJCCP.2019070103

Chicago

Balogun, Jeremiah Ademola, et al. "Ensemble Model for the Risk of Anemia in Pediatric Patients With Sickle Cell Disorder," International Journal of Computers in Clinical Practice (IJCCP) 4, no.2: 33-59. http://doi.org/10.4018/IJCCP.2019070103

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Abstract

Anemia is a major cause of morbidity and mortality of SCD patients in many parts of the world with the burden much higher in Sub Saharan Africa. This study developed an ensemble of machine learning algorithm for the prediction of the risk of anemia in pediatric SCD patients. Data for this study was collected from 115 pediatric SCD outpatients receiving treatment at a tertiary hospital in South-Western Nigeria. This study adopted a stack-ensemble model composed of deep neural network (DNN), multi-layer perceptron (MLP), and support vector machines (SVM) as base and meta-classifiers using the WEKA software. The ensemble models were compared following the stack-ensemble developed using SVM as a meta-classifier had the best performance with an accuracy of 72.7%. The study concluded that information about socio-demographic and clinical data can be used to assess the risk of anemia among SCD patients.

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