An Ensemble Model of Machine Learning Algorithms for the Severity of Sickle Cell Disease (Scd) Among Paediatrics Patients

  • Balogun Jeremiah Ademola Obafemi Awolowo University
  • Aderounmu Temilade Obafemi Awolowo University
  • Egejuru Ngozi Chidozie
  • Idowu Peter Adebayo Obafemi Awolowo University
Keywords: Sickle Cell Disease (SCD), Disease severity, Stack-Ensemble Model, Naïve Bayes, Decision Trees, Multi-Layer Perceptron

Abstract

This study was motivated at developing an ensemble of 3 supervised machine learning algorithms for the assessment of the severity of sickle cell disease (SCD) among paediatric patients. The study collected data from a tertiary hospital in south-western Nigeria following the identification of variables required for assessing the severity of SCD. The study also adopted the use of 3 supervised machine learning algorithms namely: naïve Bayes (NB), C4.5 decision trees (DT) and support vector machines (SVM) for creating the ensemble model using a 10-fold cross validation technique. The models were created by adopting the algorithms in isolation and in combination of 2 and 3 which were compared. The developed models were evaluated in order to present the model with the best performance. The results of the study showed that using an ensemble of DT and NB alone provided the best performance. The study has implications in presenting a model for improving the assessment of the severity of SCD among paediatric patients in Nigeria.

Author Biographies

Balogun Jeremiah Ademola, Obafemi Awolowo University

Department of Computer Science and Engineering,

Obafemi Awolowo University, Ile-Ife, Nigeria

Aderounmu Temilade, Obafemi Awolowo University

Department of Paediatrics and Child Health,

Obafemi Awolowo University Teaching Hospital Complex (OAUTHC),

Ile-Ife, Nigeria.

Egejuru Ngozi Chidozie

Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria.

Idowu Peter Adebayo, Obafemi Awolowo University

Department of Computer Science and Engineering,

Obafemi Awolowo University, Ile-Ife, Nigeria.

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Published
2018-11-13
Section
Research Articles