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

Autor:	Peter Adebayo Idowu, F.A. Oladeji, Olawale Olaniyi, Samuel Ademola Adegoke, Jeremiah Ademola Balogun, Adanze Onyenonachi Asinobi
Rok vydání:	2019
Předmět:	Pediatrics medicine.medical_specialty medicine.anatomical_structure Ensemble forecasting Anemia business.industry hemic and lymphatic diseases Cell medicine medicine.disease business
Zdroj:	International Journal of Computers in Clinical Practice. 4:33-59
ISSN:	2470-8534 2470-8526
DOI:	10.4018/ijccp.2019070103
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::af3bcd9c845d943e4a336d2cf84e227a https://doi.org/10.4018/ijccp.2019070103 Zobrazit plný text záznamu