phat 24(1):

Research Article

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

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  • @ARTICLE{10.4108/eetpht.9.3913,
        author={Oluwafisayo Babatope Ayoade and Tinuke Omolewa Oladele and Agbotiname Lucky Imoize and Jerome Adetoye Adeloye and Joseph Bambidele Awotunde and Segun Omotayo Olorunyomi and Oulsola Theophilius Faboya and Ayorinde Oladele Idowu},
        title={An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        keywords={Sickle Cell Disease, Erythrocytes, Machine Learning Algorithms, Ensemble Models, Health Information System},
  • Oluwafisayo Babatope Ayoade
    Tinuke Omolewa Oladele
    Agbotiname Lucky Imoize
    Jerome Adetoye Adeloye
    Joseph Bambidele Awotunde
    Segun Omotayo Olorunyomi
    Oulsola Theophilius Faboya
    Ayorinde Oladele Idowu
    Year: 2024
    An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears
    DOI: 10.4108/eetpht.9.3913
Oluwafisayo Babatope Ayoade1,*, Tinuke Omolewa Oladele2, Agbotiname Lucky Imoize3, Jerome Adetoye Adeloye2, Joseph Bambidele Awotunde2, Segun Omotayo Olorunyomi4, Oulsola Theophilius Faboya1, Ayorinde Oladele Idowu1
  • 1: Bamidele Olumilua University of Education
  • 2: University of Ilorin
  • 3: University of Lagos
  • 4: Ekiti State Data Center
*Contact email:


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.