An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

Authors

  • Oluwafisayo Babatope Ayoade Bamidele Olumilua University of Education https://orcid.org/0000-0003-2116-8059
  • Tinuke Omolewa Oladele University of Ilorin image/svg+xml
  • Agbotiname Lucky Imoize University of Lagos image/svg+xml
  • Jerome Adetoye Adeloye University of Ilorin image/svg+xml
  • Joseph Bambidele Awotunde University of Ilorin image/svg+xml
  • Segun Omotayo Olorunyomi Ekiti State Data Center
  • Oulsola Theophilius Faboya Bamidele Olumilua University of Education
  • Ayorinde Oladele Idowu Bamidele Olumilua University of Education

DOI:

https://doi.org/10.4108/eetpht.9.3913

Keywords:

Sickle Cell Disease, Erythrocytes, Machine Learning Algorithms, Ensemble Models, Health Information System

Abstract

INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient..

OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions.

METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance..

RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models.

CONCLUSION: Thus, the study concluded that involving hybridized ML algorithms in medical datasets increased predictions performance as it removed the challenges of high variance, low accuracy and feature noise and biases of medical datasets. The paper concluded that ensemble classifiers should be considered to improve sickle cell disease predictions.

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Published

19-09-2023

How to Cite

1.
Ayoade OB, Oladele TO, Lucky Imoize A, Adeloye JA, Awotunde JB, Olorunyomi SO, Faboya OT, Idowu AO. An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 19 [cited 2023 Oct. 4];9. Available from: https://publications.eai.eu/index.php/phat/article/view/3913