Identifying Early Anemia Using Machine Learning Algorithm

Abstract

This study explores the relationship between between Interleukin-6 (IL6) and Interleukin-8 (IL8) cytokines and auto-immune reactions in Sickle Cell Anemia (SCA) patients, aiming to predict their presence based on genetic factors like Haptoglobin alleles using artificial neural networks. The study, conducted on 60 SCA patients and 74 healthy individuals, found a significant association between Haptoglobin alleles and IL6/IL8 production, achieving an accuracy of 90.9% and an r-squared value of 0.88. Concurrently, the broader context of anemia as a global health issue, particularly affecting mothers and children, underscores the importance of non-invasive detection methods,like those based on machine learning and deep learning techniques. These methods, exemplified
by convolutional neural networks (CNNs) in blood analysis, offer efficient and cost-effective avenues for early diagnosis and treatment of anemia, highlighting the pivotal role of artificial intelligence in healthcare advancements.