Abstract: Sickle Cell Anemia (SCA) is a hereditary condition causing abnormal red blood cells, leading to severe health complications. Traditional treatment approaches for SCD often involve reactive management, which can delay appropriate interventions and worsen patient outcomes. The aim of this study is to leverage machine learning (ML) algorithms, including Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT), to identify high-risk groups among SCA patients using clinical and pathological data from King Abdulaziz University Hospital. This study employs a comprehensive dataset comprising 200 SCA patients, with data preprocessing to handle missing values and feature selection techniques to enhance model performance. The dataset is divided into training and testing sets, and models are evaluated using ten-fold cross-validation. Performance metrics such as True Positive Rate (TPR), False Negative Rate (FNR), Positive Predictive Value (PPV), and False Discovery Rate (FDR) are used to assess model effectiveness. The results indicate that the SVM model with the top seven correlated features achieved the highest TPR and PPV, along with the lowest FNR and FDR, demonstrating its superior performance in identifying high-risk patients. The study concludes that ML models, particularly SVM, can significantly improve risk assessment and patient management in SCA, offering a proactive tool for healthcare providers. The main message is the potential of ML algorithms to enhance clinical decision-making and improve outcomes for patients with SCA.

Haneen Banjar, Nofe Alganmi, Hajar Alharbi, Ahmed Barefah, Hatem Alahwal, Salwa Alnajjar, Abdulrahman Alboog, Salem Bahashwan and Galila Zaher, “Utilizing Machine Learning to Identify High-Risk Groups in Sickle Cell Anemia” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160670

@article{Banjar2025,
title = {Utilizing Machine Learning to Identify High-Risk Groups in Sickle Cell Anemia},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160670},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160670},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Haneen Banjar and Nofe Alganmi and Hajar Alharbi and Ahmed Barefah and Hatem Alahwal and Salwa Alnajjar and Abdulrahman Alboog and Salem Bahashwan and Galila Zaher}
}